Journal of Systems and Software最新文献

{"title":"A methodology for Electrics/Electronics platform release management in the automotive domain","authors":"Lennart Holsten ,&nbsp;Jacob Krüger ,&nbsp;Thomas Leich","doi":"10.1016/j.jss.2025.112605","DOIUrl":"10.1016/j.jss.2025.112605","url":null,"abstract":"<div><div>Platform strategies, originating from pure hardware platforms, have proven effective in optimizing development processes in the automotive domain. Over time, software-driven innovations have emerged as the primary origin of novel features in automotive systems, as exemplified by functionalities like lane-keeping assistants, traffic-sign recognition, and the prospect of autonomous driving. To address the growing importance of software, automotive manufacturers progressively incorporate principles of software-platform engineering, for instance, by adopting software product lines. However, a notable gap still persists in thoroughly managing all facets of a cyber–physical automotive system, which involves hardware, software, electronics, variability, and their complex interactions. Efforts to address this gap and to achieve holistic platform management have resulted in electrics/electronics platforms to emerge in the automotive domain; but these platforms and transitioning towards them have not been fully worked out, yet. In this article, we contribute to addressing this gap by proposing a methodology for holistic electrics/electronics platform release management. We present detailed explanations of our methodology and its individual guidelines, which we derived from practical requirements and subsequently validated through expert assessments. Specifically, we conducted a series of workshops involving eight practitioners at a large international automotive manufacturer. Our methodology and insights can help other researchers and practitioners who work on adopting electrics/electronics platform management and emphasize possible directions for future research.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112605"},"PeriodicalIF":4.1,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User experience with adaptive user interfaces: Comparing performance and preferences","authors":"Daniel Gaspar-Figueiredo ,&nbsp;Jean Vanderdonckt ,&nbsp;Silvia Abrahão ,&nbsp;Emilio Insfran","doi":"10.1016/j.jss.2025.112598","DOIUrl":"10.1016/j.jss.2025.112598","url":null,"abstract":"<div><div>Adaptive user interfaces dynamically change their content, presentation, and behavior to optimize the user experience, which has been primarily evaluated using classic usability measures but to a lesser extent by using neurological measures. While the perceived preference of specific user interface elements, such as graphical adaptive menus, has already been studied, no consensus exists regarding their performance and how to substitute a static menu with an adaptive one. To gain insights into how graphical adaptive menus could influence the user experience and to identify any correlation between users’ performance and their preferences, we conducted an experiment in which forty participants used twenty graphical adaptive menus while their brain activity was captured by employing electroencephalography to derive four measures (<em>i.e.</em>, cognitive load, engagement, attraction, and memorization). User performance was measured using task completion time, specifically the time to select menu items. Statistical analysis suggested which graphical adaptive menus were significantly better or worse than the static menu, our baseline. These results are used as the basis to suggest implications for software developers and researchers to design more effective adaptive user interfaces.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112598"},"PeriodicalIF":4.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Evolution of Technical Debt from DevOps to Generative AI: A multivocal literature review","authors":"Sergio Moreschini ,&nbsp;Elvira-Maria Arvanitou ,&nbsp;Elisavet-Persefoni Kanidou ,&nbsp;Nikolaos Nikolaidis ,&nbsp;Ruoyu Su ,&nbsp;Apostolos Ampatzoglou ,&nbsp;Alexander Chatzigeorgiou ,&nbsp;Valentina Lenarduzzi","doi":"10.1016/j.jss.2025.112599","DOIUrl":"10.1016/j.jss.2025.112599","url":null,"abstract":"<div><h3>Background:</h3><div>The rapid integration of Artificial Intelligence (AI) – including Machine Learning (ML) and Generative AI – into software systems is reshaping the software development lifecycle. As AI-driven systems become more dynamic and complex, traditional approaches to Technical Debt (TD) management face increasing limitations. Simultaneously, AI-assisted development introduces new forms of TD, particularly in relation to maintainability, explainability, and data governance.</div></div><div><h3>Objective:</h3><div>This study aims to explore how Technical Debt Management (TDM) must adapt in the context of AI-enhanced software development. It investigates (1) the evolution of TD in AI-driven systems, and (2) the implications of using AI technologies within the software engineering process.</div></div><div><h3>Methods:</h3><div>We conducted a multivocal literature review, combining insights from both peer-reviewed research and industry sources. Following established guidelines, we systematically analyzed 61 primary sources, categorized TD types and management activities, and identified key challenges and practices emerging in the AI era.</div></div><div><h3>Results:</h3><div>Our findings reveal that data-related, infrastructure, and pipeline-related TD are particularly prevalent in ML systems. Machine Learning Operations (MLOps) practices are increasingly recognized as essential for managing such debt, especially in relation to dynamic data dependencies and model retraining. In parallel, AI-generated artifacts and automated pipelines introduce new governance and maintainability challenges.</div></div><div><h3>Conclusion:</h3><div>Technical Debt in AI systems demands continuous, automated, and cross-functional management strategies. As software evolves in response to data and usage, new operational paradigms – grounded in practices like MLOps and Small Language Model Operations (SLMOps) – will be vital to ensure long-term software sustainability. This study provides a foundational map for researchers and practitioners navigating the intersection of AI and TD management.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112599"},"PeriodicalIF":4.1,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A large scale survey of motivation in software development","authors":"Idan Amit,&nbsp;Dror G. Feitelson","doi":"10.1016/j.jss.2025.112596","DOIUrl":"10.1016/j.jss.2025.112596","url":null,"abstract":"<div><h3>Context:</h3><div>Motivation is known to improve performance. In software development, in particular, there has been considerable interest in the motivation of contributors to open-source.</div></div><div><h3>Objective:</h3><div>We would like to predict motivation, in various settings. We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relative effect on motivation using supervised learning.</div></div><div><h3>Method:</h3><div>We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11-point scale. We also conducted a follow-up survey, enabling investigation of motivation improvement given improvement in motivators.</div></div><div><h3>Results:</h3><div>Predictive analysis — investigating how diverse motivators influence the probability of high motivation — provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict an increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.</div></div><div><h3>Conclusions:</h3><div>All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivation improvement, and each motivator sheds light on a different aspect of motivation. Models based on multiple motivators predict <em>motivation improvement</em> with up to 94% accuracy, better than any single motivator.</div><div><em>Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board</em>.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112596"},"PeriodicalIF":4.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model-based solution for automated (Re-)engineering of task-oriented chatbots","authors":"Sara Pérez-Soler ,&nbsp;Esther Guerra ,&nbsp;Juan de Lara","doi":"10.1016/j.jss.2025.112600","DOIUrl":"10.1016/j.jss.2025.112600","url":null,"abstract":"<div><div>Chatbots are popular to access all sorts of software services via natural language conversation. The increasing demand for task-oriented chatbots has triggered the proposal of many tools for their construction, like Dialogflow, Lex, Rasa, or Watson. However, selecting the most appropriate one is difficult; the conceptual design behind a chatbot may become buried under the tool technicalities; and migration between chatbot development platforms must be done manually. To alleviate these problems, we propose a platform-independent design notation for task-oriented chatbots, based on the analysis of fifteen chatbot development platforms. Following model-driven engineering principles, the chatbot implementation is synthesised from the design, and designs can be extracted from the implementations, enabling the migration and re-engineering of chatbots. Moreover, a recommender suggests the most suitable platform for a given chatbot design, considering contextual factors. We have realised these ideas in <span>Conga</span>: an extensible web application featuring a design notation editor; a development platform recommender; platform-specific validators; and generators and parsers for Dialogflow and Rasa. We evaluated <span>Conga</span> over 291 Dialogflow and Rasa open-source chatbots, showing its expressiveness, portability, and usefulness for finding chatbot quality issues (found in 93,8% of the chatbots). Overall, our architecture enables neutral chatbot designs, automates migration, and provides mechanisms for defect detection at the design level.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112600"},"PeriodicalIF":4.1,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why do women pursue a Ph.D. in Computer Science?","authors":"Erika Ábrahám ,&nbsp;Miguel Goulão ,&nbsp;Milena Vujošević Janičić ,&nbsp;Sarah Jane Delany ,&nbsp;Amal Mersni ,&nbsp;Oleksandra Yeremenko ,&nbsp;Özge Büyükdağlı ,&nbsp;Karima Boudaoud ,&nbsp;Caroline Oehlhorn ,&nbsp;Ute Schmid ,&nbsp;Christina Büsing ,&nbsp;Helen Bolke-Hermanns ,&nbsp;Kaja Köhnle ,&nbsp;Matilde Pato ,&nbsp;Deniz Sunar Cerci ,&nbsp;Larissa Schmid","doi":"10.1016/j.jss.2025.112586","DOIUrl":"10.1016/j.jss.2025.112586","url":null,"abstract":"<div><h3>Context:</h3><div>Computer science, even now, attracts a small number of women, and the proportion of women in the field decreases through advancing career stages. Consequently, few women progress to Ph.D. studies in computer science after completing master’s studies. Empowering women at this stage in their careers is essential, not just for equality reasons, but to unlock untapped potential for society, industry and academia.</div></div><div><h3>Objective:</h3><div>This paper aims to identify students’ career assumptions and information related to Ph.D. studies focused on gender-based differences. We propose a program to inform female master students about Ph.D. studies that explains the process, clarifies misconceptions, and alleviates concerns.</div></div><div><h3>Method:</h3><div>An extensive survey was conducted to identify factors that encourage and discourage students from undertaking Ph.D. studies. The analysis identified statistically significant differences between those who undertook Ph.D. studies and those who did not, as well as statistically significant gender differences. A catalogue of questions to initiate discussions with potential Ph.D. students which allowed them to explore these factors was developed. These were structured into a <em>Women’s Career Lunch</em> program where students can explore and discuss the benefits of Ph.D. study.</div></div><div><h3>Results:</h3><div>Encouraging factors towards Ph.D. study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in computer science in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards Ph.D. studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the Ph.D. process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a Ph.D. have less confidence in their technical skills than males but a higher preference for interdisciplinary areas. Female students are less inclined than males to perceive the industry as offering better job opportunities and more flexible career paths than academia.</div></div><div><h3>Conclusions:</h3><div>The insights collected from the survey facilitated the development of a questions catalogue structured into the <em>Women Career Lunch</em> program to help students make a more informed decision concerning whether they should pursue a Ph.D. in computer science. Localised versions of this program, in 8 languages, were created to support its adoption in different countries and assist in mitigating the female under-representation challenge.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112586"},"PeriodicalIF":4.1,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A large-scale study on developer engagement and expertise in Configurable Software System projects","authors":"Karolina M. Milano ,&nbsp;Wesley K.G. Assunção ,&nbsp;Bruno B.P. Cafeo","doi":"10.1016/j.jss.2025.112575","DOIUrl":"10.1016/j.jss.2025.112575","url":null,"abstract":"<div><div>Modern systems operate in multiple contexts making variability a fundamental aspect of Configurable Software Systems (CSSs). Variability, implemented via pre-processor directives (e.g., <span>#ifdef</span> blocks) interleaved with other code and spread across files, complicates maintenance and increases error risk. Despite its importance, little is known about how variable code is distributed among developers or whether conventional expertise metrics adequately capture variable code proficiency. This study investigates developers’ engagement with variable versus mandatory code, the concentration of variable code workload, and the effectiveness of expertise metrics in CSS projects. We mined repositories of 25 CSS projects, analyzing 450,255 commits from 9,678 developers. Results show that 59% of developers never modified variable code, while about 17% were responsible for developing and maintaining 83% of it. This indicates a high concentration of variable code expertise among a few developers, suggesting that task assignments should prioritize these specialists. Moreover, conventional expertise metrics performed poorly—achieving only around 55% precision and 50% recall in identifying developers engaged with variable code. Our findings highlight an unbalanced distribution of variable code responsibilities and underscore the need to refine expertise metrics to better support task assignments in CSS projects, thereby promoting a more equitable workload distribution.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112575"},"PeriodicalIF":4.1,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experiences and challenges from a software ecosystem for cyber–physical systems development: An empirical study on industry-academia collaboration","authors":"Vittoriano Muttillo ,&nbsp;Romina Eramo ,&nbsp;Johan Cederbladh ,&nbsp;Per Erik Strandberg ,&nbsp;Adnan Ashraf","doi":"10.1016/j.jss.2025.112579","DOIUrl":"10.1016/j.jss.2025.112579","url":null,"abstract":"<div><div>Software Ecosystem (SECO) has emerged as a crucial concept, which represents a collaborative and interconnected environment in which a variety of actors engage in developing software systems. SECOs play a key role in the development of Cyber–Physical Systems (CPSs), that present a myriad of challenges, primarily due to the need for real-time responsiveness, reliability, security, and interoperability. The implications of leveraging SECOs for developing CPSs are profound in both research and practice. This paper aims to understand the collaboration between industry and academia within SECOs for the development of CPSs, identifying potential challenges and providing insights and guidelines for the proper management of these collaborations. We conducted a systematic literature review (SLR), complemented by empirical evidence collected through an opinion survey administered to the partners of the European collaborative project AIDOaRt, a concrete example of a SECO, which worked on the development of CPSs. From these findings we discuss the identified challenges, and potential effects on collaboration, in addition to our lessons learned in the AIDOaRt project and SECO.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112579"},"PeriodicalIF":4.1,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The icing on the cake: Enhancing Code Pre-trained models-based program repair with multi-source inputs and fix templates","authors":"Shanggui Zhan ,&nbsp;Xingqi Wang ,&nbsp;Dan Wei ,&nbsp;Xinjian Cao ,&nbsp;Bin Chen","doi":"10.1016/j.jss.2025.112590","DOIUrl":"10.1016/j.jss.2025.112590","url":null,"abstract":"<div><div>Automated Program Repair (APR) aims to automatically repair software bugs with minimal or no human intervention, enhancing software reliability. Template-based approaches have been widely studied among APR techniques and have shown promising results. However, these methods are limited by the scope of predefined templates, making it challenging to handle out-of-template bugs. To this end, researchers have integrated Code Pre-trained Models (CodePTMs) to augment template-based APR. Existing methods typically rely on a single representation of the source code, which limits their ability to capture complex semantic and syntactic features. To address this issue, we propose FTPR, a novel approach that combines multi-source inputs (i.e., comments, code segments, and ASTs) and fix templates to improve CodePTM-based APR. Our evaluation on Defects4J-v1.2 shows that FTPR outperforms previous state-of-the-art approaches, successfully fixing 85 bugs. Furthermore, we validate the generality of FTPR on Defects4J-v2.0 and QuixBugs, the latter of which includes two programming languages. FTPR fixes 53 and 32 bugs on these datasets, respectively, demonstrating the significant potential and value of applying multi-source inputs and fix templates to automated program repair.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112590"},"PeriodicalIF":4.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid approach for multilevel multi-class requirement classification: Impact of stop-word removal and data augmentation","authors":"Jasleen Kaur ,&nbsp;Chanchal Roy","doi":"10.1016/j.jss.2025.112594","DOIUrl":"10.1016/j.jss.2025.112594","url":null,"abstract":"<div><div>Requirement classification in software engineering is essential for effective development. Automating this process reduces human effort and enhances decision-making. Previous studies experimented with machine learning and deep learning models to classify requirements. This novel research fills that gap by evaluating transformer-based models and a proposed Hybrid Stacked Model for multilevel, multi-class classification task. To address the limitations of existing software requirement datasets (imbalanced dataset, insufficient granularity, real world examples), we combined instances from the PROMISE_exp dataset, PURE corpus, and 20 manually collected software requirement specifications (SRS) documents using a Boolean keyword search to create a multilevel, multi-class dataset. These 3072 combined requirements are organized into a two-level hierarchy: Level 1 (functional (FR)/non-functional (NFR)); Level 2 (FRs: core functional (CFR)/derived functional (DFR)/system integration (SI)/external dependency (ED); NFRs: product (PR)/organizational (OR)/external (ER)). We applied BERT-based context-aware text augmentation to address class imbalance by expanding the dataset to 3343 instances. This study also investigates the effects of domain-specific stopword removal and text augmentation on model performance. Results show that text augmentation boosts accuracy by 0.2–3.76% across all models. Stopword removal enhances precision and recall by reducing noise, but it slightly lowers overall accuracy due to the loss of some semantic cues. The proposed Hybrid Stacked Model outperformed all pre-trained transformer models, achieving the highest accuracy of 96.77% at Level 1 and 83.06% at Level 2. A statistical t-test confirms the significance of these improvements. These findings emphasize the importance of hybrid models and domain-specific data preprocessing in enhancing requirement classification, with practical implications for automating early-stage software engineering tasks.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112594"},"PeriodicalIF":4.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}