ABSTRACT Although JavaScript dominates modern software development, research on its quality attributes remains scarce, despite the fundamental differences that distinguish it from other languages. This motivates dedicated research related to JavaScript quality attributes and metrics. This paper aims to identify (a) the quality attributes of the JavaScript language that are mainly studied and (b) the quality metrics that are used to quantify them. Additionally, the paper provides information on the tools that can be used to measure quality metrics. To achieve these goals, we have conducted a mapping study on seven journals and eight conferences of high quality. A total of 142 primary studies, published between 2002 and February 2025, have been selected and analyzed, to identify and classify software metrics to high‐level quality attributes, as described in ISO/IEC 25010:2011. Maintainability, Security, Reliability, and Usability quality attributes are the most studied ones. Furthermore, 78 generic and 48 JavaScript‐specific metrics were identified. A wide dispersion of metrics has been identified for assessing each quality attribute, based on different development tasks. Moreover, a variety of tools and benchmarks were identified. A clear research trend in JavaScript quality assessment related to issues that involve software reuse, code testing, and dynamic code analysis has been identified. Yet differences among primary studies in quality assessment and quantification, along with tool adoption indicate the need for further exploration of these recurring topics.

JavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.

JavaScript (JS) is one of the most popular programming languages on GitHub. Most JavaScript applications are reusing third-party components to acquire various functionalities. Despite the benefits offered by software reuse there are still challenges, during the evolution of JavaScript applications, related to the management and maintenance of the third-party dependencies. Our key objective is to explore the evolution of library dependencies constraints in the context of JavaScript applications in terms of (a) the changeability (i.e., number of removed, added, or maintained libraries) (b) the update frequency of the library dependencies. For this purpose, we conducted a case study on the 86 most forked JavaScript applications hosted on GitHub and analyzed reuse data from a total of 2.363 successive releases. In general, 39% of the packages introduced in the first version of the project are being reused in the entire project’s lifetime. The number of package dependencies slightly grows over time, while several other are being permanently removed. Regarding the evolution of third-party applications, it is observed that developers do not update the dependencies constraints to a most recent version, waiting to reach probably “breaking points” when the updates will be inevitable.

Abstract Technical debt management is of paramount importance for the software industry, since maintenance is the costlier activity in the software development lifecycle. In this article, we present the SDK4ED platform that enables efficient technical debt management (i.e., measurement, evolution analysis, prevention, etc.) at the code level, and evaluate its capabilities in an industrial setting. The SDK4ED platform is the outcome of a 3‐year project, including several software industries. Since, the research rigor of the approaches that reside in SDK4ED have already been validated, in this work we focus: (a) on the presentation of the platform per se; (b) the evaluation of its industrial relevance; (c) the usability of the platform; as well as (d) the financial implications of its usage.

While the technology of Deep Learning (DL) is a powerful tool when properly trained for image analysis and classification applications, some factors for its optimization rely solely on the training data and their environment. In an effort to tackle the problem of knowledge bias created during the training process of a Deep Neural Network (DNN) and specifically Adversarial Networks for image augmentation, this work presents an entirely unsupervised methodology for discovering the unfairness level of Deep Learning (DL) models and in extend, its wrongly accumulated or biased classes. Fdi, the proposed evaluation metric for quantizing the level of unfairness of a model is introduced, along with the method of weighting the model’s knowledge and producing its weakest aspects in a data-agnostic way.

Precision Agriculture (PA) nowadays adopts IoT technology, such as wireless sensors and unmanned aerial vehicles aiming at boosting production and monetary profits by controlling corps inputs such as water, fertilizers or pesticides and minimizing manual labor. Despite the fact that the benefits of Precision Agriculture techniques are widely recognized, we observe that in Europe they are adopted mainly by large farms located in UK that can afford the investment. On the contrary, in countries where the agriculture land is highly fragmented, such as Greece and Italy, the adoption of Precision Agriculture techniques is still very low. In this paper we argue that precision agriculture can be affordable even by small farms, when carefully choosing and deciding upon the technologies that will be adopted. For this reason we developed and present a tool that can help farmers cheek easily and reliably whether investing in precision farming technologies is feasible and profitable for their business.

JavaScript nowadays is among the most popular programming languages, used for developing web and IoT applications. Currently, the majority of JavaScript applications is reusing third-party components to acquire various functionalities. In this paper we isolate popular reused components and explore the type of functionality that is mostly being reused. Additionally, we examine whether the client applications adapt to the most recent versions of the reused components, and further study the reuse intensity of pairs of components that coexist in client applications. For this purpose, we performed a case study on 9389 components reused by 430 JavaScript applications hosted in GitHub. The results show that Compiler and Testing Frameworks are the most common types of functionality being reused, while the majority of client applications tend to adopt the recent versions of the reused components.

Unmanned Aerial Vehicles (UAVs) and smart sensors are the tools towards the fifth agricultural revolution. Remote sensing is thriving in agriculture, broadening the horizons of cultivators and farming practitioners. However, adopting such a technological endeavour in a raw production process is a challenging task for farmers. Operation and maintenance of such systems require specific ICT knowledge. There is also a wide variety of software and hardware equipment to choose from that can greatly impact business costs and system performance according to the kind of cultivation. Due to the lack of guidance regarding the employment of precision agriculture monitoring systems, this paper proposes a detailed decision model regarding the requirements and considerations of deploying remote sensing capabilities on a cultivation. Agricultural businesses are in need of guidance when it comes to the adoption of technological advancements especially in the case when a carefully planned operation can produce a significant amount of profits.

The major challenge that a developer confronts when building IoT systems is the management of a plethora of technologies implemented with various constraints, from different manufacturers, that at the end need to cooperate. In this paper we argue that developers can benefit from IoT frameworks by reusing their components so as to build in less time and effort IoT systems that can easily integrate new technologies. In order to explore the reuse opportunities offered by IoT frameworks we have performed a case study and analyzed 503 components reused by 35 IoT projects. We examined (a) the types of functionality that are most facilitated for reuse (b) the reuse strategy that is most adopted (c) the quality of the reused components . The results of the case study suggest that the main functionality reused is the one related to the Device Management layer and that Black-box reuse is the main type. Moreover, the quality of the reused components is improved compared to the rest of the components built from scratch.

Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT based monitoring systems. To guide the process of designing and implementing Smart farming monitoring systems, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, the energy consumption restriction. Moreover, we present and discuss the technologies that incorporate the four layers of the architecture model that are the Sensor Layer, the Network Layer, the Service Layer and the Application Layer. A discussion is also conducted upon the challenges that smart farming monitoring systems face.

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Precision Agriculture enabling the acquisition of real-time environmental data. IoT devices like Unmanned Aerial Vehicles (UAVs) equipped with cameras, sensors, and GPS receivers can deliver a variety of IoT services and applications related to fields management, by capturing images from great heights. However, there are many issues to be resolved before the effective use of UAVs in the agriculture domain, including the data collection and processing methods. There is still no standardized workflow and processes for most UAV-based applications for Precision Agriculture. In this paper, we summarize the data acquisition methods and technologies to acquire images in UAV-based Precision Agriculture and appoint the benefits and drawbacks of each one. We also review popular data analysis methods of remotely sensed imagery and discuss the outcomes of each method and its potential application in the farming operations.

Context: Technical Debt (TD) quantification has been studied in the literature and is supported by various tools; however, there is no common ground on what information shall be presented to stakeholders. Similarly to other quality monitoring processes, it is desirable to provide several views of quality through a dashboard, in which metrics concerning the phenomenon of interest are displayed. Objective: The aim of this study is to investigate the indicators that shall be presented in such a dashboard, so as to: (a) be meaningful for industrial stakeholders, (b) present all necessary information, and (c) be simple enough so that stakeholders can use them. Method: We explore TD Management (TDM) activities (i.e., measurement, prioritization, repayment) and choose the main concepts that need to be visualized, based on existing literature and toolsupport. Next, we perform a survey with 60 software engineers (i.e., architects, developers, etc.) working for 11 software development companies located in 9 countries, to understand their needs for TDM. Results / Conclusions: The results of the study suggest that different stakeholders need a different view of the quality dashboard, but also some commonalities can be identified. For example, on the one hand, managers are mostly interested in financial concepts, whereas on the other hand developers are more interested in the nature of the problems that exist in the code. The outcomes of this study can be useful to both researchers and practitioners, in the sense that the former can focus their efforts on aspects that are meaningful to industry, whereas the latter to develop meaningful dashboards, with multiple views.

Software evolution analysis can shed light on various aspects of software development and maintenance. Up to date, there is little empirical evidence on the evolution of JavaScript (JS) applications in terms of maintainability and changeability, even though JavaScript is among the most popular scripting languages for front-end web applications, including IoT applications. In this study, we investigate JS applications’ quality and changeability trends over time by examining the relevant Laws of Lehman. We analyzed over 7,500 releases of JS applications and reached some interesting conclusions. The results show that JS applications continuously change and grow, there are no clear signs of quality degradation while the complexity remains the same over time, despite the fact that the understandability of the code deteriorates.

Successful software project survival and progress over time is highly dependent on effectively managing the maintenance process. Estimating accurately maintenance process factors like the maintenance effort and the level of changes required for a new release is considered a crucial task for allocating resources. In this work we examine the maintenance process factors of JavaScript applications, which at the moment are understudied despite the need of language specific maintenance models. Furthermore we propose two maintenance indices for estimating the changes and the effort required for maintaining JavaScript applications by considering a variety of maintenance drivers. We evaluated the proposed indices through a case study on 5,788 releases coming from 60 popular JavaScript applications. The results show that project activity factors (i.e., number of open bugs and number of corrective maintenance activities) are important maintenance drivers. The proposed indices are evaluated in terms of predictive and discriminative power and both achieve high accuracy.

Secondary studies are vulnerable to threats to validity. Although, mitigating these threats is crucial for the credibility of these studies, we currently lack a systematic approach to identify, categorize and mitigate threats to validity for secondary studies. In this paper, we review the corpus of secondary studies, with the aim to identify: (a) the trend of reporting threats to validity, (b) the most common threats to validity and corresponding mitigation actions, and (c) possible categories in which threats to validity can be classified. To achieve this goal we employ the tertiary study research method that is used for synthesizing knowledge from existing secondary studies. In particular, we collected data from more than 100 studies, published until December 2016 in top quality software engineering venues (both journals and conference). Our results suggest that in recent years, secondary studies are more likely to report their threats to validity. However, the presentation of such threats is rather ad hoc, e.g., the same threat may be presented with a different name, or under a different category. To alleviate this problem, we propose a classification schema for reporting threats to validity and possible mitigation actions. Both the classification of threats and the associated mitigation actions have been validated by an empirical study, i.e., Delphi rounds with experts. Based on the proposed schema, we provide a checklist, which authors of secondary studies can use for identifying and categorizing threats to validity and corresponding mitigation actions, while readers of secondary studies can use the checklist for assessing the validity of the reported results.

Abstract Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well‐known machine learning (ML) techniques, Bayesian networks (BNs), and association rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (ie, effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy.

Abstract To capitalize upon the benefits of software reuse, an efficient selection among candidate reusable assets should be performed in terms of functional fitness and adaptability. The reusability of assets is usually measured through reusability indices. However, these do not capture all facets of reusability, such as structural characteristics, external quality attributes, and documentation. In this paper, we propose a reusability index (REI) as a synthesis of various software metrics and evaluate its ability to quantify reuse, based on IEEE Standard on Software Metrics Validity. The proposed index is compared with existing ones through a case study on 80 reusable open‐source assets. To illustrate the applicability of the proposed index, we performed a pilot study, where real‐world reuse decisions have been compared with decisions imposed by the use of metrics (including REI). The results of the study suggest that the proposed index presents the highest predictive and discriminative power; it is the most consistent in ranking reusable assets and the most strongly correlated to their levels of reuse. The findings of the paper are discussed to understand the most important aspects in reusability assessment (interpretation of results), and interesting implications for research and practice are provided.

Smart City Application Engineering is a challenging task due to the constantly evolving environment in which these applications operate and the variability of the different types of technologies that synthesize them. Therefore, flexibility and extendibility are two important quality attributes that should be taken into consideration when designing Smart City Applications. In this paper, we propose the Smart City Application Modeling Framework (SCAMF) for analyzing and designing Smart City applications that is based on the concept of Clean Architecture and adopts the representation formalism of feature models. SCAMF methodology is evaluated through a case study on a Smart Retail Platform. Quality indices like flexibility, extendibility along with metrics as complexity, cohesion and design size are compared to the initial version of the application that was completely re-engineered due to maintenance problems. The results of the study suggest that the proposed methodology improves quality indices like flexibility and extendibility up to 120%.

Abstract Software cost estimation is a key process in project management. Estimations in the initial project phases are made with a lot of uncertainty that influences estimation accuracy which typically increases as the project progresses in time. Project data collected during the various project phases can be used in a progressive time‐dependent fashion to train software cost estimation models. Our motivation is to reduce uncertainty and increase confidence based on the understanding of patterns of effort distributions in development phases of real‐world projects. In this work, we study effort distributions and suggest a four‐stage progressive software cost estimation model, adjusting the initial effort estimates during the development life‐cycle based on newly available data. Initial estimates are reviewed on the basis of the experience gained as development progresses and as new information becomes available. The proposed model provides an early, a post‐planning, a post‐specifications, and a post‐design estimate, while it uses industrial data from the ISBSG (R10) dataset. The results reveal emerging patterns of effort distributions and indicate that the model provides effective estimations and exhibits high explanatory value. Contributions in lessons learned and practical implications are also provided.

This article proposes an approach for improving the software process of a small/medium company. The methodology is presented through a case study during which estimation models have been applied, evaluated and introduced in a telecommunication software development process. The proposed methodology uses Bayesian Belief Networks to represent the relationships among implementation, product and process metrics and their impact on the development effort. The estimation models that were derived were applied and evaluated on the on-going projects of the company. Finally, by performing the same analysis on data from the International Software Benchmarking Standards Group (ISBSG) repository, it is demonstrated how one company can utilize data from other companies when it lacks sufficient data of its own. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially automatically discretizes the number of defects into a number of fault classes, then learns a model that predicts the fault class of a software system. Finally, RvC transforms the class output of the model back into a numeric prediction. This approach includes uncertainty in the models because apart from a certain number of faults, it also outputs an associated interval of values, within which this estimate lies, with a certain confidence. To evaluate this approach we perform a comparative experimental study of the effectiveness of several machine learning algorithms in a software dataset. The data was collected by Pekka Forselious and involves applications maintained by a bank of Finland.

In spite of numerous traditional and agile software project management models proposed, process and project modeling still remains an open issue. This paper proposes a Bayesian network (BN) approach for modeling software project management antipatterns. This approach provides a framework for project managers, who would like to model the cause-effect relationships that underlie an antipattern, taking into account the inherent uncertainty of a software project. The approach is exemplified through a specific BN model of an antipattern. The antipattern is modeled using the empirical results of a controlled experiment on extreme programming (XP) that investigated the impact of developer personalities and temperaments on communication, collaboration-pair viability and effectiveness in pair programming. The resulting BN model provides the precise mathematical model of a project management antipattern and can be used to measure and handle uncertainty in mathematical terms