Climate change affects many aspects of our lives. To reduce greenhouse gas emissions and increase quality of life, novel solutions need to be implemented. The digital transformation can support this direction. In this paper, we share experiences from the transformation of an urban district and show how digital solutions can support climate-friendly behavior. Our focus is on a Serious Game we developed, called “MiniLautern”, where players implement novel mobility solutions and have to address the concerns and needs of predefined stakeholders in order to maximize their score regarding stakeholders' happiness and positive environmental impact. We analyzed player feedback in order to understand better whether such a Serious Game is suitable for communicating the positive effects of novel mobility measures and researchers' efforts to address stakeholder concerns. Until now, the game MiniLautern has been completed by around 160 players, who have provided a total of 72 star ratings and answered about 20 questionnaires. So far, the reception of the game has been predominantly positive. Players like the core concept of matching novel mobility measures to stakeholder concerns, but would like to have expanded game mechanics, a larger solution space, and deeper interaction with the game.

Background: Distributed data-intensive systems are increasingly designed to be only eventually consistent. Persistent data is no longer processed with serialized and transactional access, exposing applications to a range of potential concurrency anomalies that need to be handled by the application itself. Controlling concurrent data access in monolithic systems is already challenging, but the problem is exacerbated in distributed systems. To make it worse, only little systematic engineering guidance is provided by the software architecture community regarding this issue. Aims: In this paper, we report on our study of the effectiveness and applicability of the novel design guidelines we are proposing in this regard. Method: We used action research and conducted it in the context of the software architecture design process of a multi-site platform development project. Results: Our hypotheses regarding effectiveness and applicability have been accepted in the context of the study. The initial design guidelines were refined throughout the study. Thus, we also contribute concrete guidelines for architecting distributed data-intensive systems with eventually consistent data. The guidelines are an advancement of Domain-Driven Design and provide additional patterns for the tactical design part. Conclusions: Based on our results, we recommend using the guidelines to architect safe eventually consistent systems. Because of the relevance of distributed data-intensive systems, we will drive this research forward and evaluate it in further domains.

One aspect of smart cities is the change in mobility that is urgently needed due to environmental pollution and global warming. The aim is to find ways in which the digital transformation can support smart cities in this endeavor. In this context, an app game was developed that presents new mobility concepts and enables players to decide which mobility measures should be activated in a virtual environment reflecting a concrete smart city district. In the game, a citizens' council consisting of a representative group of citizens reacts to the selected mobility measures and shows how satisfied they are with the players’ decisions. Also, social implications of such (sometimes) difficult decisions are shown. The players realize that there is no easy way of change, but also that many new mobility concepts already exist that can support cities in becoming greener.

Nowadays, fast time to market and high quality need to be considered in the development of mobile apps. One relevant quality aspect is user experience. An essential part of user experience are user emotions. However, feedback tools usually do not provide information about a user's emotions. In this paper, we present an approach that utilizes newly available 3D-sensing cameras on smartphones. We built a model where facial muscles were connected to corresponding action units and subsequently to six basic emotions. With this, we were able to automatically identify the emotions of users while they were using an app. This will help developers understand what users like and do not like regarding their apps. Such feedback can then be used as an additional source of information for subsequent app development activities.

User feedback and usage data are a valuable source of knowledge for requirements engineering (RE) and software evolution. Recent research in RE has focused on the acquisition and analysis of such user input. However, we argue that important information complementing existing data has been neglected, namely users' emotions shown during app usage, which provide insight into how users experience the app. We are aware that tracking users' emotions at large-scale might be challenging. So far, emotion tracking is often applied in lab and test settings only. We envision an unobtrusive emotion tracker running on the users' smartphones in their regular usage context. However, we do not know whether such an approach would be accepted by the app users and which requirements such an emotion-tracking solution would need to fulfill. In this paper, we present the first results from our exploratory interview study with eight mobile app users regarding their preferences and concerns with respect to emotion tracking. Early results are encouraging and suggest that app users tend to accept emotion tracking during app usage, having only minor privacy concerns. In this paper, we discuss our ongoing research, including the long-term direction and prospects of considering users' emotions for RE.

The quality of mobile applications is one essential factor for their success because users often immediately discard applications with insufficient quality. In prior contributions, we presented FIT4Apps, a quality assurance method for mobile applications, which focuses inspections and tests on the development of mobile applications. We performed a two-step empirical evaluation of this method: a controlled experiment and a case study, which was performed as a post-mortem analysis. The empirical evaluation showed the applicability of FIT4Apps and demonstrated that it finds, respectively prevents, at least 75% more mobile-specific failures during development compared to state-of-the-practice approaches. This comprises failures revealed by testing or prevented by revealed requirements defects and leads to at least 85% less mobile-specific failures after the release of the mobile application. In our setting, the effort for applying FIT4Apps is less than 1% of the overall development effort for an iteration and may even save effort considering the benefits of earlier findings.

The acceptance of mobile applications is highly dependent on the realized set of features and on the quality of the application. Information about their acceptance can be gained quickly by collecting and analyzing user feedback such as explicit textual reviews provided by an application's users or implicitly provided usage data. With an approach based on developing a minimal set of functions in order to realize a minimum viable product (MVP), it is possible to put a product on the market within a short amount of time. Currently, the elicitation, analysis, and processing of user feedback is unfocused and takes too much time and effort to mitigate the poor quality of the application. Hence, we outline an approach named Opti4Apps, which is aimed at tailored quality assurance as part of MVP development and enables and expands the benefits of an MVP by providing a semiautomated feedback elicitation, analysis, and processing framework. This is intended to raise the effectiveness and efficiency of early user feedback consideration during further development in order to assure the quality and acceptance of an app as an MVP. We will present the overall structure as well as the process behind the Opti4Apps framework. As proof-ofconcept, we implemented an initial prototype of our idea, focused on textual user feedback.

The quality of mobile applications is one essential factor for their success because users discard applications of insufficient quality immediately. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Missing mobile-specific testing methods and time and effort constraints are challenges that mobile application developing companies have to cope with nowadays. We therefore propose a mobile-specific approach that supports doing inspections and testing by reading scenarios that focus on typical mobile issues. Scenarios providing detailed support for inspectors regarding mobile-specific problems are used during an inspection to check quality early and produce mobile-specific test cases that can be used directly during testing. In order to reduce the effort for quality assurance, automation is used. Hence, our contribution in this paper is an approach for generating those scenarios. As proof of concept, we implemented a scenario generator that realizes the automated provision of scenarios considering the concrete project scope. This is intended to enable the use of reading scenarios in order to focus mobile application quality assurance.

Mobile applications have become highly pervasive in recent years. The quality of mobile applications for business use, in particular, is relevant for all stakeholders since application failures can lead to serious consequences, such as damage of corporate reputation or financial loss. For other applications, a reasonable level of quality is also required to convince users to use them. The goal of this work is to identify approaches that address the issue of quality assurance for mobile applications. We present an overview of the identified approaches based on certain viewpoints, such as the focused test level and the addressed quality, and show current research challenges. In order to drive the systematic mapping study, we derived seven research questions based on the stated goal. Then two researchers identified 3,192 records from four digital libraries based on a search string related to terms regarding quality assurance for mobile applications and predefined selection criteria. Ultimately, 230 articles were selected. We created clustered views to answer our seven research questions. In addition, we used surveys found to complement our overview of current challenges. The results show an overall upward trend of publications since 2003. Important topics include automation of GUI tests and assurance of non-functional qualities. Aspects of future research could be the establishment of test environments and the focus on defects addressing stronger the specific characteristics of mobile applications.

With an approach for mobile application development that is based on developing a minimal set of functions in order to realize a minimum viable product (MVP), it is possible to put a product on the market within a short amount of time. Nevertheless, this beneficial short time to market often involves poor quality of the developed application. Currently, the elicitation, analysis, and processing of user feedback are unfocused and take too much time and effort to mitigate the poor quality of the application. Hence, we outline an approach aimed at tailored quality assurance as part of MVP development, which enables and expands the benefits of MVP by providing a semi-automated feedback elicitation, analysis, and processing framework. This is intended to raise the effectiveness and efficiency of early user feedback consideration during further development in order to assure the quality and acceptance of a minimalistically developed mobile application.

ABSTRACT Inspections and testing are two of the most commonly performed software quality assurance processes today. Typically, these processes are applied in isolation, which, however, fails to exploit the benefits of systematically combining and integrating them. In consequence, tests are not focused on the basis of early defect detection data. Expected benefits of such process integration include higher defect detection rates or reduced quality assurance effort. Moreover, when conducting testing without any prior information regarding the system's quality, it is often unclear how to focus testing. A systematic integration of inspection and testing processes requires context‐specific knowledge about the relationships between inspections and testing. This knowledge is typically not available and needs to be empirically identified and validated. Often, context‐specific assumptions can be seen as a starting point for generating such knowledge. On the basis of the integrated inspection and testing approach, In2Test, which uses inspection data to focus testing, we present in this article how knowledge about the relationship between inspections and testing can be gained, documented, and evolved in an analytical or empirical manner. In addition, this article gives an overview of related work and highlights future research directions. Copyright © 2012 John Wiley & Sons, Ltd.

A well-known approach for identifying defect-prone parts of software in order to focus testing is to use different kinds of product metrics such as size or complexity. Although this approach has been evaluated in many contexts, the question remains if there are further opportunities to improve test focusing. One idea is to identify other types of information that may indicate the location of defect-prone software parts. Data from software inspections, in particular, appear to be promising. This kind of data might already lead to software parts that have inherent difficulties or programming challenges, and in consequence might be defect-prone. This article first explains how inspection and product metrics can be used to focus testing activities. Second, we compare selected product and inspection metrics commonly used to predict defect-prone parts (e.g. size and complexity metrics, inspection defect content metrics, and defect density metrics). Based on initial experience from two case studies performed in different environments, the suitability of different metrics for predicting defect-prone parts is illustrated. The studies revealed that inspection defect data seems to be a suitable predictor, and a combination of certain inspection and product metrics led to the best prioritizations in our contexts. In addition, qualitative experience is presented, which substantiates the expected benefit of using inspection results to optimize testing.

Inspections and testing are two of the most commonly performed software quality assurance processes today. Typically, these processes are applied in isolation, which, however, fails to exploit the benefits of systematically combining and integrating them. Expected benefits of such process integration are higher defect detection rates or reduced quality assurance effort. Moreover, when conducting testing without any prior information regarding the system's quality, it is often unclear which parts or which defect types should be prioritized. Existing approaches do not explicitly use information from inspections in a systematical way to focus testing processes. In this article, we present an integrated two-stage approach that routes inspection data to test processes in order to prioritize code classes and defect types. While an initial version of the approach focused on prioritizing code classes, this article focuses on the prioritization of defect types for testing. Results from a case study where the approach was applied on the code level show that those defect types could be prioritized before the testing that afterwards actually showed up most often during the test process. In addition, an overview of related work and an outlook on future research directions are given.

Security inspections, especially in the early development stage, are becoming increasingly important for bringing security-relevant aspects into software systems. Nowadays, such inspections often do not focus in detail on security. The well-known and approved benefits of inspections do not exploit their full potential regarding security. Thus, we have developed the Security Goal Indicator Tree (SGIT) for eliminating existing shortcomings. SGITs are a new approach for modeling and checking security-relevant aspects during the entire software development lifecycle. This article describes the modeling of such security-goal-based trees as part of requirements engineering. Initial experience was gathered from creating SGITs in an industrial environment. After the probands of our industry partner received training on existing security models, the necessary knowledge for creating security models was collected and applied. This resulted in three context-specific SGITs discussed in this article.

Defining strategies on how to perform quality assurance (QA) and how to control such activities is a challenging task for organizations developing or maintaining software and software-intensive systems. Planning and adjusting QA activities could benefit from accurate estimations of the expected defect content of relevant artifacts and the effectiveness of important quality assurance activities. Combining expert opinion with commonly available measurement data in a hybrid way promises to overcome the weaknesses of purely data-driven or purely expert-based estimation methods. This article presents a case study of the hybrid estimation method HyDEEP for estimating defect content and QA effectiveness in the telecommunication domain. The specific focus of this case study is the use of the method for gaining quantitative predictions. This aspect has not been empirically analyzed in previous work. Among other things, the results show that for defect content estimation, the method performs significantly better statistically than purely data-based methods, with a relative error of 0.3 on average (MMRE).

Security is a crucial issue in many modern software systems and can lead to immense costs if required security goals are not fulfilled. Fewer techniques exist to address the systematic analysis and detection of security problems, especially during early development phases. Based on well-known and established inspection techniques, we investigated traditional reading support, which did not fit exactly what we needed to ensure security goals. Therefore, we developed a new kind of checklist which we call guided checklist. This kind of checklist focuses the inspector much more on how to check security goals and provides the inspector with more fine-grained support than traditional reading support. To derive such checklists, we developed a model for security goals. A continuous example shows what the security goal model looks like and how to apply the guided checklist.

Quality assurance (QA) plays a crucial role in today's software development. However, methods and models proposed in literature to support QA management suffer from several drawbacks. Many are specialized to certain activities like system test or inspections. They commonly support only one application purpose, e.g., planning or controlling, and are often applicable only after measurement data has been collected for several historical applications. To overcome these drawbacks, we developed a method that can be applied to QA activities during any phase, and which supports comprehensive quality management related tasks: improvement, planning, and controlling. To be applicable in practice, the method combines the available measurement data with expert judgment to build context-specific models. In addition, the method provides early benefits, while motivating the collection of measurement data by presenting possible improvement directions. The paper presents the general concepts behind the method and research questions to be answered in upcoming empirical studies.

Planning quality assurance (QA) activities in a systematic way and controlling their execution are challenging tasks for companies that develop software or software-intensive systems. Both require estimation capabilities regarding the effectiveness of the applied QA techniques and the defect content of the checked artifacts. Existing approaches for these purposes need extensive measurement data from his-torical projects. Due to the fact that many companies do not collect enough data for applying these approaches (es-pecially for the early project lifecycle), they typically base their QA planning and controlling solely on expert opinion. This article presents a hybrid method that combines commonly available measurement data and context-specific expert knowledge. To evaluate the method’s applicability and usefulness, we conducted a case study in the context of independent verification and validation activities for critical software in the space domain. A hybrid defect content and effectiveness model was developed for the software requirements analysis phase and evaluated with available legacy data. One major result is that the hybrid model pro-vides improved estimation accuracy when compared to applicable models based solely on data. The mean magni-tude of relative error (MMRE) determined by cross-validation is 29.6% compared to 76.5% obtained by the most accurate data-based model.