Many studies consider the relation between individual aspects of the software engineering process and bug-introduction, e.g., software testing and code review. These studies typically only identify correlations between their set of variables without accounting for interactions with external variables, such as confounding factors. Within this study, we provide a broad empirical view on practices of software development and their relation to bug-introducing changes to enable future work on causal relations between those aspects. We consider the bugs, the type of change that introduced the bug, aspects of the build process, code review, software tests, and any other discussion related to the bug that we can identify. We use a qualitative approach that first describes variables of the development process and then groups the variables based on their relations. From these groups, we deduce how their (pairwise) interactions affect bug-introducing changes. We found multiple relevant relations within the development process of bug-introducing changes. Logical groups of variables and their relations provide a framework for discovering areas of interest regarding intermediate effects in the process and confounders towards bug-introduction. Software engineering practices applied during the development of bug-introducing changes are interdependent. This work lays the foundation to understand why bugs are introduced using causal modeling, discovery, and inference.

Mobile applications play a relevant role in users' daily lives by improving and easing daily processes such as commuting or making financial transactions. The aforementioned interactions enhance the usability of commonly used services. Nevertheless, the improvements should also consider special execution environments such as weak network connections or special requirements inherited from the user's condition. Due to this, the design of mobile applications should be driven by improving the user experience. This tutorial targets the usage of inclusive and accessibility design in the development process of mobile apps. Making sure that applications are accessible to all users, regardless of disabilities, is not just about following the law or fulfilling ethical obligations; it is crucial in creating inclusive and fair digital environments. This tutorial will educate participants on accessibility principles and the available tools. They will gain practical experience with specific Android and iOS platform features, as well as become acquainted with state-of - the-art automated and manual testing tools.

Over the last 10 years, the realm of AI has experienced an explosion of revolutionary breakthroughs, transforming what seemed like a far-off dream into a reality that is now deeply embedded in our everyday lives. AI’s widespread impact is revolutionizing virtually all aspects of human life, and software engineering (SE) is no exception. As we explore this changing landscape, we are faced with questions about what the future holds for SE and how AI will reshape the roles, duties, and methodologies within the field. The introduction of these groundbreaking technologies highlights the inevitable shift toward a new paradigm, suggesting a future where AI’s capabilities may redefine the boundaries of SE, potentially even more than human input. In this article, we aim at outlining the key elements that, based on our expertise, are vital for the smooth integration of AI into SE, all while preserving the intrinsic human creativity that has been the driving force behind the field. First, we provide a brief description of SE and AI evolution. Afterward, we delve into the intricate interplay between AI-driven automation and human innovation, exploring how these two components can work together to advance SE practices to new methods and standards.

Machine learning (ML) is nowadays widely used for different purposes and with several disciplines. From self-driving cars to automated medical diagnosis, machine learning models extensively support users’ daily activities, and software engineering tasks are no exception. Not embracing good ML practices may lead to pitfalls that hinder the performance of an ML system and potentially lead to unexpected results. Despite the existence of documentation and literature about ML best practices, many non-ML experts turn towards gray literature like blogs and Q&A systems when looking for help and guidance when implementing ML systems. To better aid users in distilling relevant knowledge from such sources, we propose a recommender system that recommends ML practices based on the user’s context. As a first step in creating a recommender system for machine learning practices, we implemented Idaka. A tool that provides two different approaches for retrieving/generating ML best practices: i) an information retrieval (IR) engine and ii) a large language model. The IR-engine uses BM25 as the algorithm for retrieving the practices, and a large language model, in our case Alpaca. The platform has been designed to allow comparative studies of best practices retrieval tools. Idaka is publicly available at GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM

Mobile apps are increasingly used in daily activities. Most apps require Internet connectivity to be fully exploited. Despite the fact that global access to the Internet has improved over the years, there are still complex connectivity scenarios, including situations with zero/unreliable connectivity. In such scenarios, improper handling of Eventual Connectivity Issues may cause bugs and crashes that worsen the user experience. Even though these issues have been studied in the literature, no automatic detection techniques are available. To address the mentioned gap, we have created the open source CONAN tool. CONAN can statically detect 16 types of Eventual Connectivity Issues within Android apps; it works at the source code level and alerts developers of any connectivity issue, highlighting them directly in the IDE or generating a report explaining the detected errors. In this paper, we present the technical aspects and a video of our tool, which are publicly available at https://tinyurl.com/CONAN-lint.

Android is the most popular mobile operating system in the world, running on more than 70% of mobile devices. This implies a gigantic and very competitive market for Android apps. Being successful in such a market is far from trivial and requires, besides the tackling of a problem or need felt by a vast audience, the development of high-quality apps. As recently showed in the literature, connectivity issues (e.g., mishandling of zero/unreliable Internet connection) can result in bugs and/or crashes, negatively affecting the app's user experience. While these issues have been studied in the literature, there are no techniques able to automatically detect and report them to developers. We present CONAN, a tool able to detect statically 16 types of connectivity issues affecting Android apps. We assessed the ability of CONAN to precisely identify these issues in a set of 44 open source apps, observing an average precision of 80%. Then, we studied the relevance of these issues for developers by (i) conducting interviews with six practitioners working with commercial Android apps, and (ii) submitting 84 issue reports for 27 open source apps. Our results show that several of the identified connectivity issues are considered as relevant by practitioners in specific contexts, in which connectivity is considered a first-class feature.

Mobile apps are distributed via online markets allowing practitioners to reach users worldwide; on the other side, users select what apps are more suitable for their preferences from a large set of apps offering similar features and capabilities. To facilitate that selection process, the distribution markets have different mechanisms, such as comments, ratings, and top-listed apps, including a curated list. As it is well known, apps stores metadata can provide insights for new, popular features or fixing existing bugs, as reported in previous works. However, to the best of our knowledge, app store data have not been used to identify possible predominant characteristics of successful apps using as a reference the aforementioned top lists. Thus, in this paper, we present a study that analyzes the metadata of apps belonging to Google Play top lists during 30 weeks in 4 countries to distill features of successful apps. Unfortunately, our results suggest that apps store metadata from top list apps do not provide enough information to identify those features.

The capabilities of Android applications have been increasing along with the advancements of the underlying mobile devices. This has allowed developers to include features that require more resources and provide more complex functionality inside mobile apps. An example of this is the usage of Augmented Reality (AR) in mobile applications. AR allows developers to experiment with new immersive experiences for their users in a wide variety of application areas. AR content is often visual and it represents a challenge for users with visual impairments, especially if these features are core to the underlying application. In this paper, we present a preliminary study aimed at investigating accessibility of AR functionalities of mobile applications that are not specifically designed for users with visual impairments (i.e., applications of which these users could be part of the intended audience, but they are not the specific audience). To accomplish this, we conducted a case study with 49 individuals without visual impairment and 5 individuals that are visually impaired, who used 5 applications with AR features. Our results demonstrate that the analyzed apps lack accessibility mechanisms within their AR functionalities.

Mobile devices and apps have a primordial role in daily life, and both have supported daily activities that involve humans interaction. Nevertheless, this interaction can occur between users in different platforms (e.g., web and mobile) and devices. Because of this, developers are required to test combinations of heterogeneous interactions to ensure a correct behavior of multi-device and multi-platform apps. Unfortunately, to the best of our knowledge, there is no existing open source tool that enables testing for those cases. In this paper, we present an improved version of our tool KrakenMobile, an open source tool that enables the execution of interactive End-2-End tests between Android devices. This new version, Kraken 2.0, has new capabilities such as execution of platform-agnostic interactive End-2-End tests (e.g., web and mobile), and has been migrated from Ruby to NodeJS to improve its usability. Kraken2.0 is publicly available on GitHub (https://bit.ly/30KPFcv). Videos: https://bit.ly/3f1fRXa

Android is nowadays the most popular operating system in the world, not only in the realm of mobile devices, but also when considering desktop and laptop computers. Such a popularity makes it an attractive target for security attacks, also due to the sensitive information often manipulated by mobile apps. The latter are going through a transition in which the Android ecosystem is moving from the usage of Java as the official language for developing apps, to the adoption of Kotlin as the first choice supported by Google. While previous studies have partially studied security weaknesses affecting Java Android apps, there is no comprehensive empirical investigation studying software security weaknesses affecting Android apps considering (and comparing) the two main languages used for their development, namely Java and Kotlin. We present an empirical study in which we: (i) manually analyze 681 commits including security weaknesses fixed by developers in Java and Kotlin apps, with the goal of defining a taxonomy highlighting the types of software security weaknesses affecting Java and Kotlin Android apps; (ii) survey 43 Android developers to validate and complement our taxonomy. Based on our findings, we propose a list of future actions that could be performed by researchers and practitioners to improve the security of Android apps.

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. The case for Android apps is not an exception. Therefore, in this paper we describe the process we followed to create (i) a taxonomy of mutation operations and, (ii) two tools,MDroid+andMutAPKfor mutant generation of Android apps. To this end, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 software artifacts from different sources (e.g.,bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented them in two tools, the first enabling mutant generation at the source code level, and the second designed to perform mutations at APK level. The rationale for having a dual-approach is based on the fact that source code is not always available when conducting mutation testing. Thus, mutation testing for APKs enables new scenarios in which researchers/practitioners only have access to APK files. The taxonomy, proposed operators, and tools have been evaluated in terms of the number of non-compilable, trivial, equivalent, andduplicatemutants generated and their capacity to represent real faults in Android apps as compared to other well-known mutation tools.

Deep learning (DL) techniques are on the rise in the software engineering research community. More and more approaches have been developed on top of DL models, also due to the unprecedented amount of software-related data that can be used to train these models. One of the recent applications of DL in the software engineering domain concerns the automatic detection of software vulnerabilities. While several DL models have been developed to approach this problem, there is still limited empirical evidence concerning their actual effectiveness especially when compared with shallow machine learning techniques. In this paper, we partially fill this gap by presenting a large-scale empirical study using three vulnerability datasets and five different source code representations (i.e., the format in which the code is provided to the classifiers to assess whether it is vulnerable or not) to compare the effectiveness of two widely used DL-based models and of one shallow machine learning model in (i) classifying code functions as vulnerable or non-vulnerable (i.e., binary classification), and (ii) classifying code functions based on the specific type of vulnerability they contain (or "clean", if no vulnerability is there). As a baseline we include in our study the AutoML utility provided by the Google Cloud Platform. Our results show that the experimented models are still far from ensuring reliable vulnerability detection, and that a shallow learning classifier represents a competitive baseline for the newest DL-based models.

Developers do not always have the knowledge needed to understand source code and must refer to different resources (e.g., teammates, documentation, the web). This non-trivial process, called program comprehension, is very time-consuming. While many approaches support the comprehension of a given code at hand, they are mostly focused on defining extractive summaries from the code (i.e., on selecting from a given piece of code the most important statements/comments to comprehend it). However, if the information needed to comprehend the code is not there, their usefulness is limited. We present ADANA, an approach to automatically inject comments describing a given piece of Android code. ADANA reuses the descriptions of similar and well-documented code snippets retrieved from various online resources. Our evaluation has shown that ADANA is able to aid the program comprehension process.

Understanding software is an inherent requirement for many maintenance and evolution tasks. Without a thorough understanding of the code, developers would not be able to fix bugs or add new features timely. Measuring code understandability might be useful to guide developers in writing better code, and could also help in estimating the effort required to modify code components. Unfortunately, there are no metrics designed to assess the understandability of code snippets. In this work, we perform an extensive evaluation of 121 existing as well as new code-related, documentation-related, and developer-related metrics. We try to (i) correlate each metric with understandability and (ii) build models combining metrics to assess understandability. To do this, we use 444 human evaluations from 63 developers and we obtained a bold negative result: none of the 121 experimented metrics is able to capture code understandability, not even the ones assumed to assess quality attributes apparently related, such as code readability and complexity. While we observed some improvements while combining metrics in models, their effectiveness is still far from making them suitable for practical applications. Finally, we conducted interviews with five professional developers to understand the factors that influence their ability to understand code snippets, aiming at identifying possible new metrics.

In theory, (good) documentation is an invaluable asset to any software project, as it helps stakeholders to use, understand, maintain, and evolve a system. In practice, however, documentation is generally affected by numerous shortcomings and issues, such as insufficient and inadequate content and obsolete, ambiguous information. To counter this, researchers are investigating the development of advanced recommender systems that automatically suggest high-quality documentation, useful for a given task. A crucial first step is to understand what quality means for practitioners and what information is actually needed for specific tasks.

Mobile markets allow developers to easily distribute mobile apps worldwide and collect complaints and feature requests in the form of user reviews and star ratings. Therefore, internationalization (i18n) of apps is a highly desired feature, which is currently supported in mobile platforms by using resources flies with strings that can be internationalized manually. This manual translation can be a time consuming and error-prone task when the app is targeted for different languages and the amount of strings to be internationalized is large. Moreover, the lack of consideration of the impact of internationalized, strings can drive to collateral (i.e., unexpected) changes and bugs in the GUI layout of apps. In this paper, we present an empirical study on how i18n can impact the GUIs of Android apps. In particular, we investigated the changes, bugs and bad practices related to GUIs when strings of a given default language (i.e., English in this case) are translated to 7 different languages. To this, we created a source- codeless approach, ITDroid, for automatically (i) translating strings, and (ii) detecting bad practices and collateral changes introduced in the GUIs of Android apps after translation. ITDroid was used on a set of 31 Android apps and their translated versions. Then, we manually validated the i18n changes that introduced bugs into the GUIs of the translated apps. Based on these results, we present a taxonomy of i18n changes and bugs found along in the apps as well as implications of our findings for practitioners and researchers. Online appendix: https://thesoftwaredesignlab.github.io/ITDroid/.

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://bit.ly/2uIkXs8. Dataset: https://bit.ly/2Wu9qsn

Mutation testing is a time consuming process because large sets of fault-injected-versions of an original app are generated and executed with the purpose of evaluating the quality of a given test suite. In the case of Android apps, recent studies even suggest that mutant generation and mutation testing effort could be greater when the mutants are generated at the APK level. To reduce that effort, useless (e.g., equivalent) mutants should be avoided and mutant selection techniques could be used to reduce the set of mutants used with mutation testing. However, despite the existence of mutation testing tools, none of those tools provides features for removing useless mutants and sampling mutant sets. In this paper, we present MutAPK 2.0, an improved version of our open source mutant generation tool (MutAPK) for Android apps at APK level. To the best of our knowledge, MutAPK 2.0 is the first tool that enables the removal of dead-code mutants, provides a set of mutant selection strategies, and removes automatically equivalent and duplicate mutants. MutAPK 2.0 is publicly available at GitHub: https://thesoftwaredesignlab.github.io/MutAPK/ VIDEO: https://thesoftwaredesignlab.github.io/MutAPK/video.html

(Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavourable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners.

Mobile developers constantly have to deal with users pressure for continuous delivery of apps while keeping quality attributes such as confidentiality and data integrity. To better support developers in testing security vulnerabilities during evolution and maintenance of mobile apps, in this demo we present a novel tool, OPIA, for on-device security testing. OPIA allows developers/testers to (i) conduct SQL-injection attacks and collect logs to identify leaks of sensitive information through record-and-replay testing, and (ii) extract data stored in local databases and shared preferences to identify sensitive information that is not properly encrypted, anonymized. OPIA is publicly available at GitHub.

Mobile applications that involve the interaction of two or more users are becoming more common nowadays, and the demand of good performance and availability by their users is increasing. This makes testing and automation of processes essential for delivering high-quality cross-device apps. In this paper, we present Kraken, a cross-device testing tool that enables a tester to write, run, and validate test scenarios that involve the interaction of more than two devices as well as its underlying implementation. The proposed tool uses APKs as input along with tests scripts wrote using the Gherkin syntax. Kraken is publicly available on GitHub https://thesoftwaredesignlab.github.io/KrakenMobile/ Videos: https://www.youtube.com/playlist?list=PLF5U8kfVgRcJ3RCHt7cWmwlqN93brbVW-

Universal design principles aim to improve accessibility by ensuring product designs consider all users, including those with certain disabilities (e.g., visual impairments). In the case of mobile apps, accessibility is mostly provided by existing features in mobile devices, like TalkBack on Android that reads information to users. However, it is not clear to what extent developers actually implement universal design principles or utilize these technologies to support accessibility of their applications. By performing a mining-based pilot study, we observed developers seldom use Accessibility APIs and there is a limited usage of assistive descriptions. Then, we focused on understanding the perspective of developers through an investigation of posts from StackOverflow. We identified the aspects of accessibility that developers implemented as well as experienced difficulty (or lack of understanding). We performed a formal open-coding of 366 discussions threads with multi-author agreement to create a taxonomy regarding the aspects discussed by developers with respect to accessibility in Android. From the qualitative analysis, we distilled lessons to guide further research and actions in aiding developers with supporting users that require assistive features.

The amount of Android application is having a tremendous increasing trend, exerting pressure over practitioners and researchers around application quality, frequent releases, and quick fixing of bugs. This pressure leads practitioners to make usage of automated approaches based on using source-code as input. Nevertheless, third-party services are not able to use these approaches due to privacy factors. In this paper we present MutAPK, an open source mutation testing tool that enables the usage of APK as input for this task. MutAPK generates mutants without the need of having access to source code, because the mutations are done in an intermediate representation of the code (i.e., SMALI) that does not require compilation. MutAPK is publicly available at GitHub: https://bit.ly/2KYvgP9 VIDEO: https://bit.ly/2WOjiyy.

Android apps are inextricably linked to the official Android APIs. Such a strong form of dependency implies that changes introduced in new versions of the Android APIs can severely impact the apps' code, for example because of deprecated or removed APIs. In reaction to those changes, mobile app developers are expected to adapt their code and avoid compatibility issues. To support developers, approaches have been proposed to automatically identify API compatibility issues in Android apps. The state-of-the-art approach, named CiD, is a data-driven solution learning how to detect those issues by analyzing the changes in the history of Android APIs ("API side" learning). While it can successfully identify compatibility issues, it cannot recommend coding solutions. We devised an alternative data-driven approach, named ACRYL. ACRYL learns from changes implemented in other apps in response to API changes ("client side" learning). This allows not only to detect compatibility issues, but also to suggest a fix. When empirically comparing the two tools, we found that there is no clear winner, since the two approaches are highly complementary, in that they identify almost disjointed sets of API compatibility issues. Our results point to the future possibility of combining the two approaches, trying to learn detection/fixing rules on both the API and the client side.

Software licenses dictate how source code or binaries can be modified, reused, and redistributed. In the case of open source projects, software licenses generally fit into two main categories, permissive and restrictive, depending on the degree to which they allow redistribution or modification under licenses different from the original one(s). Developers and organizations can also modify existing licenses, creating custom licenses with specific permissive/restrictive terms. Having such a variety of software licenses can create confusion among software developers, and can easily result in the introduction of licensing bugs, not necessarily limited to well-known license incompatibilities. In this work, we report a study aimed at characterizing licensing bugs by (i) building a catalog categorizing the types of licensing bugs developers and other stakeholders face, and (ii) understanding the implications licensing bugs have on the software projects they affect. The presented study is the result of the manual analysis of 1,200 discussions related to licensing bugs carried out in issue trackers and in five legal mailing lists of open source communities. Our findings uncover new types of licensing bugs not addressed in prior literature, and a detailed assessment of their implications.

Mobile software development involves significant challenges to developers such as device fragmentation (i.e., enormous hardware and software diversity), event-driven programming (i.e., programming based on user interactions, sensor readings and other events where the program must react) and continuous evolving platforms (i.e., fast changing mobile frameworks and technologies). This can lead programmers to error-prone code, because of the multiple combinations of external variables that must be taken into account in an app development process. Thus, testing is an underlying necessity in mobile applications to deliver high quality apps. However, defining tests suites for app development is a difficult task that requires a lot of effort, because it must consider all the possible states of an app, its context (e.g., device in which is running, sensors, touch gestures, screen proportions, connectivity), and a large combination of mobile devices and operating systems. Previous efforts have been done to extract models that support automated testing. However, as of today there is not a single model that synthesizes different aspects in mobile apps such as domain, usage, context and GUI-related information. These aspects represent complementary information that can be mixed into a single and enriched model. In this paper, we propose a multi-model representation that combines information extracted statically and dynamically from Android apps. Our approach allows practitioners to automatically extract augmented models that combine different types of information, and could help them during comprehension and testing tasks.

Mutation testing is a technique in which faults (mutants) are injected into a program or application to assess its test suite effectiveness. It works by inserting mutants and running the application’s test suite to identify if the mutants are detected (killed) or not (survived) by the tests. Although computationally expensive, it has proven to be an effective method to assess application test suites. Several mutation testing frameworks and tools have been built for the various programing languages, however, very few tools have been built for the JavaScript language, more specifically, there is a lack of mutation testing tools for the Node.js runtime and npm based applications. The npm Registry is a public collection of modules of open-source code for Node.js, front-end web applications, mobile applications, robots, routers, and countless other needs of the JavaScript community. The over 700,000 packages hosted in npm are downloaded more than 5 billion times per week. More and more software is published in npm every day, representing a huge opportunity to share code and solutions, but also to share bugs and faulty software. In this paper, we briefly describe prior work for mutation operators in JavaScript and Node.js, and propose Mutode, an open source tool which leverages the npm package ecosystem to perform mutation testing for JavaScript and Node.js applications. We empirically evaluated Mutode effectiveness by running it on 12 of the top 20 npm modules that have automated test suites.

Techniques to automatically identify the stereotypes of different software artifacts (e.g., classes, methods, commits) were previously presented. Those approaches utilized the techniques to support comprehension of software artifacts, but those stereotype-based approaches were not designed to consider the structure and purpose of unit tests, which are widely used in software development to increase the quality of source code. Moreover, unit tests are different than production code, since they are designed and written by following different principles and workflows.

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.

Optimizing mobile apps early on in the development cycle is supposed to be a key strategy for obtaining higher user rankings, more downloads, and higher retention. However, little research has been done with respect to identifying and understanding actual optimization practices performed by developers. In this paper, we present the results of three empirical studies aimed at investigating practices of Android developers towards improving apps performance, by means of micro-optimizations.

Abstract Unreadable code could compromise program comprehension, and it could cause the introduction of bugs. Code consists of mostly natural language text, both in identifiers and comments, and it is a particular form of text. Nevertheless, the models proposed to estimate code readability take into account only structural aspects and visual nuances of source code, such as line length and alignment of characters. In this paper, we extend our previous work in which we use textual features to improve code readability models. We introduce 2 new textual features, and we reassess the readability prediction power of readability models on more than 600 code snippets manually evaluated, in terms of readability, by 5K+ people. We also replicate a study by Buse and Weimer on the correlation between readability and FindBugs warnings, evaluating different models on 20 software systems, for a total of 3M lines of code. The results demonstrate that (1) textual features complement other features and (2) a model containing all the features achieves a significantly higher accuracy as compared with all the other state‐of‐the‐art models. Also, readability estimation resulting from a more accurate model, ie, the combined model, is able to predict more accurately FindBugs warnings.

The number of mobile devices sold worldwide has exponentially increased in recent years, surpassing that of personal computers in 2011. Such devices daily download and run millions of apps that take advantage of modern hardware features (e.g., multi-core processors, large Organic Light-Emitting Diode—OLED—screens, etc.) to offer exciting user experiences. Clearly, there is a cost to pay in terms of energy consumption and, in particular, of reduced battery life. This has pushed researchers to investigate how to reduce the energy consumption of apps, for example, by optimizing the color palette used in the app’s GUI. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this article proposes an approach, named Gui Energy Multi-objective optiMization for Android apps (GEMMA), for generating color palettes using a multi-objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. The empirical evaluation demonstrates (i) substantial improvements in terms of the three different objectives, (ii) a concrete reduction of the energy consumption as assessed by a hardware power monitor, (iii) the attractiveness of the generated color compositions for apps’ users, and (iv) the suitability of GEMMA to be adopted in industrial contexts.

From a legal perspective, software licenses govern the redistribution, reuse, and modification of software as both source and binary code. Free and Open Source Software (FOSS) licenses vary in the degree to which they are permissive or restrictive in allowing redistribution or modification under licenses different from the original one(s). In certain cases, developers may modify the license by appending to it an exception to specifically allow reuse or modification under a particular condition. These exceptions are an important factor to consider for license compliance analysis since they modify the standard (and widely understood) terms of the original license. In this work, we first perform a large-scale empirical study on the change history of over 51K FOSS systems aimed at quantitatively investigating the prevalence of known license exceptions and identifying new ones. Subsequently, we performed a study on the detection of license exceptions by relying on machine learning. We evaluated the license exception classification with four different supervised learners and sensitivity analysis. Finally, we present a categorization of license exceptions and explain their implications.

We advocate for a paradigm shift in supporting the information needs of developers, centered around the concept of automated on-demand developer documentation. Currently, developer information needs are fulfilled by asking experts or consulting documentation. Unfortunately, traditional documentation practices are inefficient because of, among others, the manual nature of its creation and the gap between the creators and consumers. We discuss the major challenges we face in realizing such a paradigm shift, highlight existing research that can be leveraged to this end, and promote opportunities for increased convergence in research on software documentation.

Enabling fully automated testing of mobile applications has recently become an important topic of study for both researchers and practitioners. A plethora of tools and approaches have been proposed to aid mobile developers both by augmenting manual testing practices and by automating various parts of the testing process. However, current approaches for automated testing fall short in convincing developers about their benefits, leading to a majority of mobile testing being performed manually. With the goal of helping researchers and practitioners - who design approaches supporting mobile testing - to understand developer's needs, we analyzed survey responses from 102 open source contributors to Android projects about their practices when performing testing. The survey focused on questions regarding practices and preferences of developers/testers in-the-wild for (i) designing and generating test cases, (ii) automated testing practices, and (iii) perceptions of quality metrics such as code coverage for determining test quality. Analyzing the information gleaned from this survey, we compile a body of knowledge to help guide researchers and professionals toward tailoring new automated testing approaches to the need of a diverse set of open source developers.

Mobile app development involves a unique set of challenges including device fragmentation and rapidly evolving platforms, making testing a difficult task. The design space for a comprehensive mobile testing strategy includes features, inputs, potential contextual app states, and large combinations of devices and underlying platforms. Therefore, automated testing is an essential activity of the development process. However, current state of the art of automated testing tools for mobile apps posses limitations that has driven a preference for manual testing in practice. As of today, there is no comprehensive automated solution for mobile testing that overcomes fundamental issues such as automated oracles, history awareness in test cases, or automated evolution of test cases.In this perspective paper we survey the current state of the art in terms of the frameworks, tools, and services available to developers to aid in mobile testing, highlighting present shortcomings. Next, we provide commentary on current key challenges that restrict the possibility of a comprehensive, effective, and practical automated testing solution. Finally, we offer our vision of a comprehensive mobile app testing framework, complete with research agenda, that is succinctly summarized along three principles: Continuous, Evolutionary and Large-scale (CEL).

Program understanding plays a pivotal role in software maintenance and evolution: a deep understanding of code is the stepping stone for most software-related activities, such as bug fixing or testing. Being able to measure the understandability of a piece of code might help in estimating the effort required for a maintenance activity, in comparing the quality of alternative implementations, or even in predicting bugs. Unfortunately, there are no existing metrics specifically designed to assess the understandability of a given code snippet. In this paper, we perform a first step in this direction, by studying the extent to which several types of metrics computed on code, documentation, and developers correlate with code understandability. To perform such an investigation we ran a study with 46 participants who were asked to understand eight code snippets each. We collected a total of 324 evaluations aiming at assessing the perceived understandability, the actual level of understanding, and the time needed to understand a code snippet. Our results demonstrate that none of the (existing and new) metrics we considered is able to capture code understandability, not even the ones assumed to assess quality attributes strongly related with it, such as code readability and complexity.

Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability, (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities, and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world.

Mutation testing has been widely used to assess the fault-detection effectiveness of a test suite, as well as to guide test case generation or prioritization. Empirical studies have shown that, while mutants are generally representative of real faults, an effective application of mutation testing requires “traditional” operators designed for programming languages to be augmented with operators specific to an application domain and/or technology. This paper proposes MDroid+, a framework for effective mutation testing of Android apps. First, we systematically devise a taxonomy of 262 types of Android faults grouped in 14 categories by manually analyzing 2,023 so ware artifacts from different sources (e.g., bug reports, commits). Then, we identified a set of 38 mutation operators, and implemented an infrastructure to automatically seed mutations in Android apps with 35 of the identified operators. The taxonomy and the proposed operators have been evaluated in terms of stillborn/trivial mutants generated as compared to well know mutation tools, and their capacity to represent real faults in Android apps

The last decade has seen tremendous proliferation of mobile computing in our society. Billions of users have access to millions of mobile applications that can be installed directly on their mobile devices and electrical appliances such as TV set-top boxes. Factors such as new monetization/revenue models, programming models, and distribution infrastructures contribute to an "attractive" movement that captivates new and traditional developers, as well as a crowd of other professionals that explore, design, and implement mobile apps [9]. Also, the need for "enterprise apps" that support start-ups or serve as a new front-end for traditional companies is pushing software related professionals to embrace the mobile technologies [9]. However, the nature of the economy (devices, apps, markets) imposes new challenges on how mobile apps are envisioned, designed, implemented, tested, released, and maintained. This technology briefing aims to help address the challenges of testing and maintaining mobile apps by providing participants from both academic and industrial backgrounds with information on the state-of-art and state-of-practice mobile testing and maintenance techniques. Specifically, we aim to highlight two things: first, new techniques and methodologies for making effective automated testing of mobile apps practical and accessible to developers, and second, open academic research questions related to such technology transfer.

Maintaining unit test cases is important during the maintenance and evolution of a software system. In particular, automatically documenting these unit test cases can ameliorate the burden on developers maintaining them. For instance, by relying on up-to-date documentation, developers can more easily identify test cases that relate to some new or modified functionality of the system. We surveyed 212 developers (both industrial and open-source) to understand their perspective towards writing, maintaining, and documenting unit test cases. In addition, we mined change histories of C# software systems and empirically found that unit test methods seldom had preceding comments and infrequently had inner comments, and both were rarely modified as those methods were modified. In order to support developers in maintaining unit test cases, we propose a novel approach - UnitTestScribe - that combines static analysis, natural language processing, backward slicing, and code summarization techniques to automatically generate natural language documentation of unit test cases. We evaluated UnitTestScribe on four subject systems by means of an online survey with industrial developers and graduate students. In general, participants indicated that UnitTestScribe descriptions are complete, concise, and easy to read.

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.

Database-centric applications (DCAs) usually rely on database operations over a large number of tables and attributes. Understanding how database tables and attributes are used to implement features in DCAs along with the constraints related to these usages is an important component of any DCA’s maintenance. However, manually documenting database related operations and their asynchronously evolving constraints in constantly changing source code is a hard and time-consuming problem. In this paper, we present a novel approach, namely DBScribe, aimed at automatically generating always up-to-date natural language descriptions of database operations and schema constraints in source code methods. DBScribe statically analyzes the code and database schema to detect database usages and then prop- agates these usages and schema constraints through the call-chains implementing database-related features. Finally, each method in these call-chains is automatically documented based on the underlying database usages and constraints.

Code reading is one of the most frequent activities in software maintenance; before implementing changes, it is necessary to fully understand source code often written by other developers. Thus, readability is a crucial aspect of source code that may significantly influence program comprehension effort. In general, models used to estimate software readability take into account only structural aspects of source code, e.g., line length and a number of comments. However, source code is a particular form of text; therefore, a code readability model should not ignore the textual aspects of source code encapsulated in identifiers and comments. In this paper, we propose a set of textual features aimed at measuring code readability. We evaluated the proposed textual features on 600 code snippets manually evaluated (in terms of readability) by 5K+ people. The results demonstrate that the proposed features complement classic structural features when predicting code readability judgments. Consequently, a code readability model based on a richer set of features, including the ones proposed in this paper, achieves a significantly higher accuracy as compared to all of the state-of-the-art readability models.

Detecting similar applications is a challenging problem, since it implies that similar high-level features and their low-level implementations can be detected and matched automatically. We propose an approach for automatically detecting Closely reLated applications in ANdroid (CLANdroid) by relying on advanced Information Retrieval techniques and five semantic anchors: identifiers, Android APIs, intents, permissions, and sensors. To evaluate CLANdroid we created a benchmark consisting of 14,450 apps along with information on similar apps provided by Google Play. We also compared effectiveness of different semantic anchors for detecting similar apps as perceived by 27 users. The results show that using Android-specific semantic anchors are useful for detecting similar Android apps across different categories. We also measured the impact of third-party libraries and obfuscated code when identifying similar Android apps, and our results suggest that there is significant difference in the accuracy when third-party libraries are excluded.

Existing approaches for automated testing of An- droid apps are designed to achieve different goals and exhibit some pros and cons that should be carefully considered by developers and testers. For instance, random testing (RT) provides a high ratio of infeasible inputs or events, and test cases generated with RT and systematic exploration-based testing (SEBT) are not representative of natural (i.e., real) application usage scenarios. In addition, collecting test scripts for automated testing is expensive. We address limitations of existing tools for GUI-based testing of Android apps in a novel hybrid approach called T+. Our approach is based on a novel framework, which is aimed at generating actionable test cases for different testing goals. The framework also enables GUI-based testing without expensive test scripts collection for the stakeholders.

During software maintenances tasks, commit messages are an important source of information, knowledge, and documentation that developers rely upon. However, the number and nature of daily activities and interruptions can influence the quality of resulting commit messages. This formal demonstration paper presents ChangeScribe, a tool for automatically generating commit messages. ChangeScribe is available at http://www.cs.wm.edu/semeru/changescribe (Eclipse plugin, instructions, demos and the source code)

Nowadays software applications, and especially mobile apps, undergo frequent release updates through app stores. After installing/updating apps, users can post reviews and provide ratings, expressing their level of satisfaction with apps, and possibly pointing out bugs or desired features. In this paper we show—by performing a study on 100 Android apps—how developers addressing user reviews increase their app's success in terms of ratings. Specifically, we devise an approach, named CRISTAL, for tracing informative crowd reviews onto source code changes, and for monitoring the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings. The results indicate that developers implementing user reviews are rewarded in terms of ratings. This poses the need for specialized recommendation systems aimed at analyzing informative crowd reviews and prioritizing feedback to be satisfied in order to increase the apps success.

Performance of rapidly evolving mobile apps is one of the top concerns for users and developers nowadays. Despite the efforts of researchers and mobile API designers to provide developers with guidelines and best practices for improving the performance of mobile apps, performance bottlenecks are still a significant and frequent complaint that impacts the ratings and apps' chances for success. However, little research has been done into understanding actual developers' practices for detecting and fixing performance bottlenecks in mobile apps. In this paper, we present the results of an empirical study aimed at studying and understanding these practices by surveying 485 open source Android app and library developers, and manually analyzing performance bugs and fixes in their app repositories hosted on GitHub. The paper categorizes actual practices and tools used by real developers while dealing with performance issues. In general, our findings indicate that developers heavily rely on user reviews and manual execution of the apps for detecting performance bugs. While developers also use available tools to detect performance bottlenecks, these tools are mostly for profiling and do not help in detecting and fixing performance issues automatically.

Software licenses legally govern the way in which developers can use, modify, and redistribute a particular system. While previous studies either investigated licensing through mining software repositories or studied licensing through FOSS reuse, we aim at understanding the rationale behind developers' decisions for choosing or changing software licensing by surveying open source developers. In this paper, we analyze when developers consider licensing, the reasons why developers pick a license for their project, and the factors that influence licensing changes. Additionally, we explore the licensing-related problems that developers experienced and expectations they have for licensing support from forges (e.g., GitHub). Our investigation involves, on one hand, the analysis of the commit history of 16,221 Java open source projects to identify the commits where licenses were added or changed. On the other hand, it consisted of a survey-in which 138 developers informed their involvement in licensing-related decisions and 52 provided deeper insights about the rationale behind the actions that they had undertaken. The results indicate that developers adopt licenses early in the project's development and change licensing after some period of development (if at all). We also found that developers have inherent biases with respect to software licensing. Additionally, reuse-whether by a non-contributor or for commercial purposes-is a dominant reason why developers change licenses of their systems. Finally, we discuss potential areas of research that could ameliorate the difficulties that software developers are facing with regard to licensing issues of their software systems.

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant software can be easily retrieved by developers from large repositories. State of the art approaches either rely on the availability of the source code, or use supervised machine learning approaches, which require a set of already labeled software as training data. These restrictions make current approaches fail when such information is not available. We propose a novel approach, which overcomes these limitations by using semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems. We evaluated our approach in a study on the Apache Foundation Repository of Java libraries and the results indicate that our approach is able to correctly identify a correct category for 86% of the libraries.

Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of various parts of a software system, predicting defects, and many other tasks. A number of existing approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages with textual contents in the bug reports. Yet, the effectiveness of these techniques is oftentimes sub optimal when commit messages are empty or only contain minimum information, this particular problem makes the process of recovering trace ability links between commits and bug reports particularly challenging. In this work, we aim at improving the effectiveness of existing bug linking techniques by utilizing rich contextual information. We rely on a recently proposed tool, namely Change Scribe, which generates commit messages containing rich contextual information by using a number of code summarization techniques. Our approach then extracts features from these automatically generated commit messages and bug reports and inputs them into a classification technique that creates a discriminative model used to predict if a link exists between a commit message and a bug report. We compared our approach, coined as RCLinker (Rich Context Linker), to MLink, which is an existing state-of-the-art bug linking approach. Our experiment results on bug reports from 6 software projects show that RCLinker can outperform MLink in terms of F-measure by 138.66%.

Software licenses determine, from a legal point of view, under which conditions software can be integrated, used, and above all, redistributed. Licenses evolve over time to meet the needs of development communities and to cope with emerging legal issues and new development paradigms. Such evolution of licenses is likely to be accompanied by changes in the way how software uses such licenses, resulting in some licenses being adopted while others are abandoned. This paper reports a large empirical study aimed at quantitatively and qualitatively investigating when and why developer change software licenses. Specifically, we first identify licenses' changes in 1,731,828 commits, representing the entire history of 16,221 Java projects hosted on GitHub. Then, to understand the rationale of license changes, we perform a qualitative analysis - following a grounded theory approach - of commit notes and issue tracker discussions concerning licensing topics and, whenever possible, try to build trace ability links between discussions and changes. Our results point out a lack of trace ability of when and why licensing changes are made. This can be a major concern, because a change in the license of a system can negatively impact those that reuse it.

Manually reproducing bugs is time-consuming and tedious. Software maintainers routinely try to reproduce unconfirmed issues using incomplete or no informative bug reports. Consequently, while reproducing an issue, the maintainer must augment the report with information - such as a reliable sequence of descriptive steps to reproduce the bug - to aid developers with diagnosing the issue. This process encumbers issue resolution from the time the bug is entered in the issue tracking system until it is reproduced. This paper presents Crash Droid, an approach for automating the process of reproducing a bug by translating the call stack from a crash report into expressive steps to reproduce the bug and a kernel event trace that can be replayed on-demand. Crash Droid manages trace ability links between scenarios' natural language descriptions, method call traces, and kernel event traces. We evaluated Crash Droid on several open-source Android applications infected with errors. Given call stacks from crash reports, Crash Droid was able to generate expressive steps to reproduce the bugs and automatically replay the crashes. Moreover, users were able to confirm the crashes faster with Crash Droid than manually reproducing the bugs or using a stress-testing tool.

Several open and closed source repositories group software systems and libraries to allow members of particular organizations or the open source community to take advantage of them. However, to make this possible, it is necessary to have effective ways of searching and browsing the repositories. Software tagging is the process of assigning terms (i.e., tags or labels) to software assets in order to describe features and internal details, making the task of understanding software easier and potentially browsing and searching through a repository more effective. We present Sally, an automatic software tagging approach that is able to produce meaningful tags for Maven-based software projects by analyzing their bytecode and dependency relations without any special requirements from developers. We compared tags generated by Sally to the ones in two widely used online repositories, and the tags generated by a state-of-the-art categorization approach. The results suggest that Sally is able to generate expressive tags without relying on machine learning-based models.

Database-centric applications (DCAs) usually contain a large number of tables, attributes, and constraints describing the underlying data model. Understanding how database tables and attributes are used in the source code along with the constraints related to these usages is an important component of DCA maintenance. However, documenting database-related operations and their constraints in the source code is neither easy nor common in practice. In this paper, we present a two-fold empirical study aimed at identifying how developers document database usages at source code method level. In particular, (i) we surveyed open source developers to understand their practices on documenting database usages in source code, and (ii) we mined a large set of open source projects to measure to what extent database-related methods are commented and if these comments are updated during evolution. Although 58% of the developers claimed to find value in method comments describing database usages, our findings suggest that 77% of 33K+ methods in 3.1K+ open-source Java projects with database accesses were completely undocumented.

GUI-based models extracted from Android app execution traces, events, or source code can be extremely useful for challenging tasks such as the generation of scenarios or test cases. However, extracting effective models can be an expensive process. Moreover, existing approaches for automatically deriving GUI-based models are not able to generate scenarios that include events which were not observed in execution (nor event) traces. In this paper, we address these and other major challenges in our novel hybrid approach, coined as MONKEYLAB. Our approach is based on the Record→Mine→Generate→Validate framework, which relies on recording app usages that yield execution (event) traces, mining those event traces and generating execution scenarios using statistical language modeling, static and dynamic analyses, and validating the resulting scenarios using an interactive execution of the app on a real device. The framework aims at mining models capable of generating feasible and fully replayable (i.e., Actionable) scenarios reflecting either natural user behavior or uncommon usages (e.g., Corner cases) for a given app. We evaluated MONKEYLAB in a case study involving several medium-to-large open-source Android apps. Our results demonstrate that MONKEYLAB is able to mine GUI-based models that can be used to generate actionable execution scenarios for both natural and unnatural sequences of events on Google Nexus 7 tablets.

Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models. Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts. We show how a particular deep learning model can remember its state to effectively model sequential data, e.g., Streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix. Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects. We experiment with two of the models' hyper parameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization. Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models. Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts. Thus, our work serves as the first step toward deep learning software repositories.

The modern software development landscape has seen a shift in focus toward mobile applications as tablets and smartphones near ubiquitous adoption. Due to this trend, the complexity of these “apps” has been increasing, making development and maintenance challenging. Additionally, current bug tracking systems are not able to effectively support construction of reports with actionable information that directly lead to a bug’s resolution. To address the need for an improved reporting system, we introduce a novel solution, called FUSION, that helps users auto-complete reproduction steps in bug reports for mobile apps. FUSION links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. The approach that FUSION employs is generalizable to other current mobile software platforms, and constitutes a new method by which off-device bug reporting can be conducted for mobile software projects. In a study involving 28 participants we applied FUSION to support the maintenance tasks of reporting and reproducing defects from 15 real-world bugs found in 14 open source Android apps while qualitatively and qualitatively measuring the user experience of the system. Our results demonstrate that FUSION both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information.

The wide diffusion of mobile devices has motivated research towards optimizing energy consumption of software systems— including apps—targeting such devices. Besides efforts aimed at dealing with various kinds of energy bugs, the adoption of Organic Light-Emitting Diode (OLED) screens has motivated research towards reducing energy consumption by choosing an appropriate color palette. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this paper proposes an approach, named GEMMA (Gui Energy Multi-objective optiMization for Android apps), for generating color palettes using a multi- objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. An empirical evaluation that we performed on 25 Android apps demonstrates not only significant improvements in terms of the three different objectives, but also confirmed that in most cases users still perceived the choices of colors as attractive. Finally, for several apps we interviewed the original developers, who in some cases expressed the intent to adopt the proposed choice of color palette, whereas in other cases pointed out directions for future improvements

The mobile apps market is one of the fastest growing areas in the information technology. In digging their market share, developers must pay attention to building robust and reliable apps. In fact, users easily get frustrated by repeated failures, crashes, and other bugs; hence, they abandon some apps in favor of their competition. In this paper we investigate how the fault- and change-proneness of APIs used by Android apps relates to their success estimated as the average rating provided by the users to those apps. First, in a study conducted on 5,848 (free) apps, we analyzed how the ratings that an app had received correlated with the fault- and change-proneness of the APIs such app relied upon. After that, we surveyed 45 professional Android developers to assess (i) to what extent developers experienced problems when using APIs, and (ii) how much they felt these problems could be the cause for unfavorable user ratings. The results of our studies indicate that apps having high user ratings use APIs that are less fault- and change-prone than the APIs used by low rated apps. Also, most of the interviewed Android developers observed, in their development experience, a direct relationship between problems experienced with the adopted APIs and the users’ ratings that their apps received.

The growing number of questions related to mobile development in StackOverflow highlights an increasing interest of software developers in mobile programming. For the Android platform, 213,836 questions were tagged with Android-related labels in StackOverflow between July 2008 and August 2012. This paper aims at investigating how changes occurring to Android APIs trigger questions and activity in StackOverflow, and whether this is particularly true for certain kinds of changes. Our findings suggest that Android developers usually have more questions when the behavior of APIs is modified. In addition, deleting public methods from APIs is a trigger for questions that are (i) more discussed and of major interest for the community, and (ii) posted by more experienced developers. In general, results of this paper provide important insights about the use of social media to learn about changes in software ecosystems, and establish solid foundations for building new recommenders for notifying developers/managers about important changes and recommending them relevant crowdsourced solutions

Some previous work began studying the relationship between application domains and quality, in particular through the prevalence of code and design smells (e.g., anti-patterns). Indeed, it is generally believed that the presence of these smells degrades quality but also that their prevalence varies across domains. Though anecdotal experiences and empirical evidence gathered from developers and researchers support this belief, there is still a need to further deepen our understanding of the relationship between application domains and quality. Consequently, we present a large-scale study that investigated the systematic relationships between the presence of smells and quality-related metrics computed over the bytecode of 1,343 Java Mobile Edition applications in 13 different application domains. Although, we did not find evidence of a correlation between smells and quality- related metrics, we found (1) that larger differences exist between metric values of classes exhibiting smells and classes without smells and (2) that some smells are commonly present in all the domains while others are most prevalent in certain domains

Energy consumption of mobile applications is nowadays a hot topic, given the widespread use of mobile devices. The high demand for features and improved user experience, given the available powerful hardware, tend to increase the apps’ energy consumption. However, excessive energy consumption in mobile apps could also be a consequence of energy greedy hardware, bad programming practices, or particular API usage patterns. We present the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles. By using a hardware power monitor, we measure energy consumption of method calls when executing typical usage scenarios in 55 mobile apps from different domains. Based on the collected data, we mine and analyze energy-greedy APIs and usage patterns. We zoom in and discuss the cases where either the anomalous energy consumption is unavoidable or where it is due to suboptimal usage or choice of APIs. Finally, we synthesize our findings into actionable knowledge and recipes for developers on how to reduce energy consumption while using certain categories of Android APIs and patterns

In the recent years, studies of design and programming practices in mobile development are gaining more attention from researchers. Several such empirical studies used Android applications (paid, free, and open source) to analyze factors such as size, quality, dependencies, reuse, and cloning. Most of the studies use executable files of the apps (APK files), instead of source code because of availability issues (most of free apps available at the Android official market are not open-source, but still can be downloaded and analyzed in APK format). However, using only APK files in empirical studies comes with some threats to the validity of the results. In this paper, we analyze some of these pertinent threats. In particular, we analyzed the impact of third-party libraries and code obfuscation practices on estimating the amount of reuse by class cloning in Android apps. When including and excluding third-party libraries from the analysis, we found statistically significant differences in the amount of class cloning 24,379 free Android apps. Also, we found some evidence that obfuscation is responsible for increasing a number of false positives when detecting class clones. Finally, based on our findings, we provide a list of actionable guidelines for mining and analyzing large repositories of Android applications and minimizing these threats to validity

Although version control systems allow developers to describe and explain the rationale behind code changes in commit messages, the state of practice indicates that most of the time such commit messages are either very short or even empty. In fact, in a recent study of 23K+ Java projects it has been found that only 10% of the messages are descriptive and over 66% of those messages contained fewer words as compared to a typical English sentence (i.e., 15-20 words). However, accurate and complete commit messages summarizing software changes are important to support a number of development and maintenance tasks. In this paper we present an approach, coined as Change Scribe, which is designed to generate commit messages automatically from change sets. Change Scribe generates natural language commit messages by taking into account commit stereotype, the type of changes (e.g., files rename, changes done only to property files), as well as the impact set of the underlying changes. We evaluated Change Scribe in a survey involving 23 developers in which the participants analyzed automatically generated commit messages from real changes and compared them with commit messages written by the original developers of six open source systems. The results demonstrate that automatically generated messages by Change Scribe are preferred in about 62% of the cases for large commits, and about 54% for small commits.

Research studies in software maintenance are notoriously hard to reproduce due to lack of datasets, tools, implementation details (e.g., parameter values, environmental settings) and other factors. The progress in the field is hindered by the challenge of comparing new techniques against existing ones, as researchers have to devote a lot of their resources to the tedious and error-prone process of reproducing previously introduced approaches. In this paper, we address the problem of experiment reproducibility in software maintenance and provide a long term solution towards ensuring that future experiments will be reproducible and extensible. We conducted a mapping study of a number of representative maintenance techniques and approaches and implemented them as a library of experiments and components that we make publicly available with TraceLab, called the Component Library. The goal of these experiments and components is to create a body of actionable knowledge that would (i) facilitate future research and would (ii) allow the research community to contribute to it as well. In addition, to illustrate the process of using and adapting these techniques, we present an example of creating new techniques based on existing ones, which produce improved results.

This paper presents a technique for automatically mining and visualizing API usage examples. In contrast to previous approaches, our technique is capable of finding examples of API usage that occur across several functions in a program. This distinction is important because of a gap between what current API learning tools provide and what programmers need: current tools extract relatively small examples from single files/functions, even though programmers use APIs to build large software. The small examples are helpful in the initial stages of API learning, but leave out details that are helpful in later stages. Our technique is intended to fill this gap. It works by representing software as a Relational Topic Model, where API calls and the functions that use them are modeled as a document network. Given a starting API, our approach can recommend complex API usage examples mined from a repository of over 14 million Java methods.

Question & answer (Q&A) websites, such as Stack Overflow (SO), are widely used by developers to find and provide answers to technical issues and concerns in software development. Mobile development is not an exception to the rule. In the latest SO dump, more than 400K questions were labeled with tags related to mobile technologies. Although, previous works have analyzed the main topics and trends in SO threads, there are no studies devoted specifically to mobile development. In this paper we used topic modeling techniques to extract hot-topics from mobile-development related questions. Our findings suggest that most of the questions include topics related to general questions and compatibility issues, and the most specific topics, such as crash reports and database connection, are present in a reduced set of questions.

During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others.

The worldwide acceptance of software as an important aspect in daily life, and the continued growth of the software development industry, have influenced the creation of new companies (small and medium size) during the last decade. The rapid pace with which the companies are founded makes them to experiment some drawbacks such as informality in the software development process, and technological deficiencies. Software development companies have found in agile methodologies a possible solution for improving their practices and processes. However, transition to agile methods is not a simple task, and the transition process should be assessed in order to know whether it is helping the company to become agile. Thus, in this paper we propose a model which could be used for measuring companies agility in four different levels: project, project management, workteam, and agile workspace coverage. Future work is related to using this model in small and medium development companies.

There is a tremendous wealth of code authorship information available in source code. Motivated with the presence of this information, in a number of open source projects, an approach to recommend expert developers to assist with a software change request (e.g., a bug fixes or feature) is presented. It employs a combination of an information retrieval technique and processing of the source code authorship information. The relevant source code files to the textual description of a change request are first located. The authors listed in the header comments in these files are then analyzed to arrive at a ranked list of the most suitable developers. The approach fundamentally differs from its previously reported counterparts, as it does not require software repository mining. Neither does it require training from past bugs/issues, which is often done with sophisticated techniques such as machine learning, nor mining of source code repositories, i.e., commits. An empirical study to evaluate the effectiveness of the approach on three open source systems, ArgoUML, JEdit, and MuCommander, is reported. Our approach is compared with two representative approaches: (1) using machine learning on past bug reports, and (2) based on commit logs. The presented approach is found to provide recommendation accuracies that are equivalent or better than the two compared approaches. These findings are encouraging, as it opens up a promising and orthogonal possibility of recommending developers without the need of any historical change information.

Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Unfortunately, for different legal and organizational reasons the source code is often not available, thus making it difficult to automatically categorize binary executables of software applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries as attributes for automatic categorization of software applications that use these API calls. API calls can be extracted from source code and more importantly, from the byte-code of applications, thus making automatic categorization approaches applicable to closed source repositories. We evaluate our approach along with other machine learning algorithms for software categorization on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source one with 745 applications. Our contribution is twofold: not only do we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, but also we carried out the first comprehensive empirical evaluation of automatic categorization approaches.