Information and Software Technology最新文献

{"title":"Unraveling the pain points of domain modeling","authors":"Isadora Valle ,&nbsp;Tiago Prince Sales ,&nbsp;Eduardo Guerra ,&nbsp;Maya Daneva ,&nbsp;Renata Guizzardi ,&nbsp;Luiz Olavo Bonino da Silva Santos ,&nbsp;Henderik A. Proper ,&nbsp;Giancarlo Guizzardi","doi":"10.1016/j.infsof.2025.107736","DOIUrl":"10.1016/j.infsof.2025.107736","url":null,"abstract":"<div><div>Conceptual models offer numerous benefits but require significant investments, requiring modelers to strive to balance costs and benefits. Understanding the modeling process and the frustrations experienced by modelers can provide valuable insights for this assessment. While research acknowledges certain instances of modelers’ dissatisfaction, its scope often limits detailed examination. This study seeks to identify and analyze the main pain points associated with domain modeling through a five-phase empirical study using a multi-method approach. We identified <strong>71</strong> pain points, synthesized them to <strong>41</strong>, and prioritized <strong>16</strong> as the most significant and prevalent in domain modeling. We then refined, documented, and exemplified the prioritized pain points, analyzed their potential causes, and discussed their practical implications. Our findings provide valuable insights for improving modelers’ experiences and optimizing the modeling process.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107736"},"PeriodicalIF":3.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843731","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":"Don’t settle for the first! How many GitHub Copilot solutions should you check?","authors":"Julian Oertel ,&nbsp;Jil Klünder ,&nbsp;Regina Hebig","doi":"10.1016/j.infsof.2025.107737","DOIUrl":"10.1016/j.infsof.2025.107737","url":null,"abstract":"<div><h3>Context:</h3><div>With the integration of generative artificial intelligence (GenAI) tools such as GitHub Copilot into development processes, developers can be supported when writing code.</div></div><div><h3>Objectives:</h3><div>As GitHub Copilot has a feature to provide up to ten solutions at once, we explore, how developers should approach those solutions with the goal of providing recommendations to achieve suitable trade-offs in finding correct solutions and checking solutions.</div></div><div><h3>Methods:</h3><div>In this study, we analyze a total of 2025 coding problems provided by LeetCode and 17<!--> <!-->048 solutions to solve these problems generated by GitHub Copilot in Python. We focus on three key issues: firstly, whether it is beneficial to consider multiple solutions; secondly, the impact of the position of a solution; and thirdly, the number of solutions that should be checked by a developer.</div></div><div><h3>Results:</h3><div>Overall, our results point to the following observations: (1) solutions are not less likely to be correct if they appear at later positions; (2) when looking for a solution to a common problem, checking four to five solutions is generally enough; (3) novel or difficult problems are unlikely to be solved by GitHub Copilot; (4) skipping the first solution is advised when considering only one solution, as the first solution is less likely to be correct; and (5) checking all solutions is necessary to not miss correct solutions, but the effort is usually not justified.</div></div><div><h3>Conclusion:</h3><div>Based on our study, we conclude that there is potential for improvement in better supporting developers. For instance, there are few cases where ten generated solutions provide more value than fewer solutions. Depending on the use scenario, it could be more useful if GitHub Copilot allowed developers to request a single, comprehensive solution.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107737"},"PeriodicalIF":3.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834796","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":"Extensive mutation for testing of word sense disambiguation models","authors":"Deping Zhang ,&nbsp;Zhaohui Yang ,&nbsp;Xiang Huang ,&nbsp;Yanhui Li","doi":"10.1016/j.infsof.2025.107734","DOIUrl":"10.1016/j.infsof.2025.107734","url":null,"abstract":"<div><h3>Context:</h3><div>Word sense disambiguation (WSD) models are extensively utilized in various translation and question-answering systems. Assessing the WSD capability of these models aids in their improvement and enhances their dependability. Recently, researchers have introduced the concept of “mutation” to induce WSD errors in machine translation systems to evaluate their WSD ability.</div></div><div><h3>Objective:</h3><div>Inspired by the recent research, this study aims to extend types of mutations and check their potential application in testing WSD models to check whether these mutations can effectively provoke WSD errors.</div></div><div><h3>Method:</h3><div>We have designed and implemented nine innovative types of mutations focusing on words, phrases, and sentence structure for the sentence in WSD testing. Based on these extensive mutations, we have proposed a WSD testing framework that utilizes large language models to prompt sentence mutations and assess the disambiguation capability of WSD models.</div></div><div><h3>Results:</h3><div>In our research, we have conducted experiments using five widely recognized test sets for WSD tasks under five widely used WSD models. The experimental results show that (a) our testing framework can produce correct mutations for nine proposed mutations, and (b) the newly developed mutations have been shown to successfully trigger a substantial number of factual and unique WSD errors.</div></div><div><h3>Conclusions:</h3><div>The new types of mutations we designed can effectively be applied in mutation-based WSD testing. This suggests that by exploring more types of mutations, more WSD errors can be triggered.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107734"},"PeriodicalIF":3.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800724","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":"Copiloting the future: How generative AI transforms Software Engineering","authors":"Leonardo Banh ,&nbsp;Florian Holldack ,&nbsp;Gero Strobel","doi":"10.1016/j.infsof.2025.107751","DOIUrl":"10.1016/j.infsof.2025.107751","url":null,"abstract":"<div><h3><strong>Context</strong></h3><div>With rapid technological advancements, artificial intelligence (AI) has become integral to various sectors. Generative AI (GenAI) tools like ChatGPT or GitHub Copilot, with their unique content creation capabilities, pose transformative potential in Software Engineering by offering new ways to optimize software development processes. However, the integration into current processes also presents challenges that require a sociotechnical analysis to effectively realize GenAI's potential.</div></div><div><h3><strong>Objective</strong></h3><div>This study investigates how GenAI can be leveraged in the domain of Software Engineering, exploring its action potentials and challenges to help businesses and developers optimize the adoption of this technology in their workflows.</div></div><div><h3><strong>Method</strong></h3><div>We performed a qualitative study and collected data from expert interviews with eighteen professionals working in Software Engineering-related roles. Data analysis followed the principles of Grounded Theory to analyze how GenAI supports developers' goals, aligns with organizational practices, and facilitates integration into existing routines.</div></div><div><h3><strong>Results</strong></h3><div>The findings demonstrate several opportunities of GenAI in Software Engineering to increase productivity in development teams. However, several key barriers were also identified, that should be accounted for in successful integrations. We synthesize the results in a grounded conceptual framework for GenAI adoption in Software Engineering.</div></div><div><h3><strong>Conclusions</strong></h3><div>This study contributes to the discourse on GenAI in Software Engineering by providing a conceptual framework that aids in understanding the opportunities and challenges of GenAI. It offers practical guidelines for businesses and developers to enhance GenAI integration and lays the groundwork for future research on its impact in software development.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107751"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817125","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":"Understanding the product knowledge acquisition process in multiprovider software evolution scenarios: An exploratory study","authors":"Anelis Pereira-Vale,&nbsp;Tomás Vera ,&nbsp;Carlos Vásquez ,&nbsp;Daniel Perovich,&nbsp;Jocelyn Simmonds,&nbsp;Sergio F. Ochoa","doi":"10.1016/j.infsof.2025.107738","DOIUrl":"10.1016/j.infsof.2025.107738","url":null,"abstract":"<div><h3>Context</h3><div>The life cycle of a custom software system involves multiple evolution projects, where each one can be carried out by a different provider, i.e., software development company. When the provider changes, the new one should understand the structure and functionality of the product to evolve, in order to determine the scope, effort, risks and uncertainties of the next evolution project. Regardless of its relevance in practice, product comprehension has received little attention, particularly in multiprovider evolution scenarios. This limits the ability of researchers and practitioners to understand and improve this process.</div></div><div><h3>Objective</h3><div>To understand the process performed by software teams to discover and assimilate the knowledge about the structure, functionality, and quality aspects of a custom system developed by another team.</div></div><div><h3>Method</h3><div>We conducted an exploratory study that answered four research questions considering two particular software evolution scenarios. The study involved interviews with 19 project leaders from Chilean and Brazilian small and medium-sized enterprises, who regularly participate in this type of project.</div></div><div><h3>Results</h3><div>For each evolution scenario, we identified: (1) the underlying structure and dynamic of the product knowledge acquisition process, (2) the knowledge gathering activities and software artifacts used by the development teams, and (3) how effective these teams were when performing these activities. The study also identifies causes of low effectiveness of teams in this process and promising research avenues to address them.</div></div><div><h3>Conclusion</h3><div>The product knowledge acquisition process is usually informal and has low cost-effectiveness. Provider switching results in an important loss of information and knowledge about the structure and functionality of the product, which limits comprehension and evolution of the system. The results show a clear need to change the status-quo in the customer-provider relationship in the study scenarios, and open several research opportunities to improve this process.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107738"},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785843","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":"Uncertainty propagation from sensor data to deep learning models in autonomous driving","authors":"Yifan Wang ,&nbsp;Tiexin Wang ,&nbsp;Tao Yue","doi":"10.1016/j.infsof.2025.107735","DOIUrl":"10.1016/j.infsof.2025.107735","url":null,"abstract":"<div><h3>Context:</h3><div>Deep learning has been widely used in Autonomous Driving Systems (ADS). Though significant progress has been made regarding their efficiency and accuracy, uncertainty remains a critical factor affecting ADS safety. Such uncertainties are often due to environmental noise and/or imperfect algorithm structures. Studies on uncertainty quantification mostly focus on single classification tasks and overlook how uncertainties propagate from the perception to downstream decision-making, studying of which is critical, as the interplay between perception and decision-making can significantly impact the overall safety of ADS.</div></div><div><h3>Objectives:</h3><div>We quantify and understand the uncertainty propagation from sensor data to deep learning models, as well as its impact on ADS safety.</div></div><div><h3>Methods:</h3><div>We present an empirical study that quantifies both aleatoric and epistemic uncertainties and assesses how such uncertainties propagate and impact ADS safety under various sensor noise conditions. We also investigate the suitability of two epistemic uncertainty quantification methods (i.e., MC Dropout and Deep Ensembles) to ADS tasks and their cost-effectiveness in selecting highly-uncertain samples.</div></div><div><h3>Results:</h3><div>Results show that increased noise can significantly increase uncertainty and degrade model performance, thereby compromising decision-making and potentially impacting ADS safety. Both MC Dropout and Deep Ensembles effectively measure the model’s epistemic uncertainty, with MC Dropout showing higher correlation with ADS safety, and saving time and computational costs. Moreover, there are significant differences in the highly-uncertain samples they identified.</div></div><div><h3>Conclusion:</h3><div>Our results show the importance of considering uncertainty propagation to ensure the ADS safety. Compared to Deep Ensembles, MC Dropout’s efficiency makes it a more suitable choice in the context of ADS.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107735"},"PeriodicalIF":3.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738113","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":"MLOps best practices, challenges and maturity models: A systematic literature review","authors":"Mohammad Zarour,&nbsp;Hamza Alzabut,&nbsp;Khalid T. Al-Sarayreh","doi":"10.1016/j.infsof.2025.107733","DOIUrl":"10.1016/j.infsof.2025.107733","url":null,"abstract":"<div><h3>Context:</h3><div>Agile and DevOps methodologies have revolutionized software development, leading to increased efficiency and reliability in product delivery. Building on this success, Machine Learning Operations (MLOps) has emerged to streamline the development and deployment of machine learning (ML) models, addressing challenges unique to ML workflows.</div></div><div><h3>Objectives:</h3><div>This study aims to explore the complexities organizations face when adopting MLOps, focusing on three main challenges: the lack of standardized practices, difficulties in maintaining model consistency and scalability, and ambiguities in assessing MLOps maturity. The study also aims to identify best practices and common pitfalls, contributing to a clearer understanding and standardization of MLOps.</div></div><div><h3>Methods:</h3><div>A comprehensive literature review was conducted, analyzing 45 articles that address MLOps best practices, challenges, maturity models, and lessons from previous implementations. This review categorizes findings to provide insights into successful and unsuccessful MLOps applications.</div></div><div><h3>Results:</h3><div>The study identifies nine best practices, eight common challenges, and five maturity models relevant to MLOps adoption. Key lessons from successful and unsuccessful MLOps implementations are outlined, with a focus on improving standardization and reducing ambiguity in MLOps practices.</div></div><div><h3>Conclusion:</h3><div>The findings highlight the importance of establishing standardized MLOps practices to address the unique challenges of machine learning workflows. By categorizing best practices, maturity models, and lessons learned, this study aims to contribute to a robust MLOps framework that enhances the reliability and scalability of machine learning in production environments.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107733"},"PeriodicalIF":3.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738109","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":"Reengineering software systems into microservices: State-of-the-art and future directions","authors":"Thakshila Imiya Mohottige ,&nbsp;Artem Polyvyanyy ,&nbsp;Colin Fidge ,&nbsp;Rajkumar Buyya ,&nbsp;Alistair Barros","doi":"10.1016/j.infsof.2025.107732","DOIUrl":"10.1016/j.infsof.2025.107732","url":null,"abstract":"<div><h3>Context:</h3><div>With the acknowledged benefits of microservices architectures, such as scalability, flexibility, improved maintenance, and deployment, legacy software systems are increasingly being reengineered into microservices. Recently, a plethora of methods, techniques, tools, and evaluation criteria for reengineering software systems into microservices have been proposed without being systematized.</div></div><div><h3>Objectives:</h3><div>The objective of this work is to conduct an in-depth systematic literature review to identify and analyze methods, techniques, and tools for reengineering software systems into microservices and the ways for evaluating such reengineering initiatives and their results.</div></div><div><h3>Methods:</h3><div>A systematic literature review of works on reengineering software systems into microservices was performed, yielding 117 primary studies. The review focused on addressing key research questions concerning the evolution of microservices reengineering, methodologies employed, tools available, and the challenges faced in the reengineering process. We used a taxonomy development method to systematize knowledge in these areas.</div></div><div><h3>Results:</h3><div>The analysis revealed multiple reengineering approaches: static, dynamic, hybrid, and artifact-driven. Significant evaluation criteria identified include coupling, cohesion, and modularity. Key paradigms for microservices reengineering, such as domain-driven design and interface analysis, were identified and discussed. The study also highlights that incremental and iterative transitions are favored in practice.</div></div><div><h3>Conclusion:</h3><div>This study provides a structured overview of the current state of research on reengineering software systems into microservices. It highlights challenges in existing reengineering methodologies. Future directions include validating behavioral equivalence of original and reengineered systems, automating microservices generation, and refining database layer partitioning. The findings emphasize the need for further work to enhance the reengineering process and evaluation of the transition between monolithic and microservices architectures.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107732"},"PeriodicalIF":3.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BTAL: An imbalance software bug report triage approach based on BERT-TextCNN","authors":"Yanmei Zhang,&nbsp;Yuhang Sun,&nbsp;Yi Shi,&nbsp;Shujuan Jiang,&nbsp;Guan Yuan","doi":"10.1016/j.infsof.2025.107731","DOIUrl":"10.1016/j.infsof.2025.107731","url":null,"abstract":"<div><div>With the expansion of software project scale, a large number of software bug reports have been generated. Bug triage is an indispensable task in software development and maintenance, which directly affects the efficiency of software bug fixing and maintenance cost. Existing bug triage methods often fail to make full use of the useful information in defect reports, resulting in the neglect of some auxiliary information that is critical to the defect triage task. Meanwhile, they also fail to take into account the uncertainty of developers’ work and the gap in their activity, thus leading to the imbalance in software defect report datasets. To address this issue, we propose an imbalance software bug report triage method, BTAL, based on BERT-TextCNN. Firstly, the method utilizes multiple information from software bug reports and employs BERT and TextCNN models for vector representation and feature extraction. Then, the output feature vectors are input into the softmax function to obtain the probabilities of bug reports being triaged to developers. To solve the problem of imbalanced datasets, we propose an adaptive loss function that can adaptively adjust the loss weights based on different categories of samples. This helps the network reduce its focus on majority classes and increase its focus on minority classes, thereby improving triage accuracy. Experimental results on five large-scale open-source software projects, namely GCC, NetBeans, Eclipse, Mozilla, and OpenOffice, demonstrated the effectiveness of the BTAL method in solving software bug triage problems, outperforming current state-of-the-art models.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107731"},"PeriodicalIF":3.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760686","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 study of Scrum @ S&P Global in the post-COVID-19 era: Unsuitable for remote work or just flawed implementation?","authors":"Adam Przybyłek ,&nbsp;Daniel Belter ,&nbsp;Kieran Conboy","doi":"10.1016/j.infsof.2025.107728","DOIUrl":"10.1016/j.infsof.2025.107728","url":null,"abstract":"<div><h3>Context:</h3><div>Although the COVID-19 pandemic is behind us, the seminal challenge for agile teams has not gone away, as “Work-From-Home (WFH) is here to stay”. Despite a wealth of literature documenting the challenges of applying agile methods in remote environments, practical guidance for overcoming these challenges remains limited. Furthermore, systematic meta-guidelines to help practitioners develop tailored, context-specific solutions are largely absent.</div></div><div><h3>Method:</h3><div>This article reports on an Action Research project conducted at S&amp;P Global 18 months after the company’s transition to a WFH model.</div></div><div><h3>Objective:</h3><div>The project aimed to evaluate existing adaptations to remote work, identify persistent issues, and develop strategies to address them.</div></div><div><h3>Results:</h3><div>Our investigation revealed that while ad-hoc solutions — such as virtualizing Scrum ceremonies and shifting informal in-person conversations to Slack channels — enabled work continuity during the pandemic, they proved insufficient for tackling more complex challenges. Intriguingly, only two ongoing issues stemmed from misalignments between Scrum and remote settings, while the remainder were rooted in flawed Scrum implementation. With our guidance, the participating team not only rediscovered the “Inspect and Adapt” principle but also mastered a systematic approach to method tailoring, enabling them to devise measures that effectively resolved most of their issues.</div></div><div><h3>Conclusion:</h3><div>Our study reinforces prior findings indicating the applicability of Scrum beyond its traditional “home ground”, while also highlighting the need for remote teams to exhibit even greater discipline in adhering to the Scrum framework to navigate the additional challenges of remote work. We also observed a heightened necessity for more comprehensive documentation in remote settings to compensate for limited immediate access to colleague assistance, which consequently restricts knowledge sharing. Finally, the structured approach we employed offers valuable guidance for software companies seeking to identify and address operational inefficiencies in both remote and hybrid work environments.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"183 ","pages":"Article 107728"},"PeriodicalIF":3.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}