Journal of Software-Evolution and Process最新文献

{"title":"Minimizing Inter-Dependencies in Functional Requirements for Timely Delivery of Software Projects: A Prioritization Approach Using AHP and Spanning Trees","authors":"Muhammad Yaseen,&nbsp;Anum Bahar,&nbsp;Gohar Rahman,&nbsp;Mirjalol Ashurov,&nbsp;Erkin Kholiyarov","doi":"10.1002/smr.70063","DOIUrl":"https://doi.org/10.1002/smr.70063","url":null,"abstract":"<div>\u0000 \u0000 <p>Requirements prioritization provides a structured way to rank and sequence requirements, which is particularly important in large-scale ERP systems where development tasks are distributed among multiple teams. Prerequisite requirements often depend on one another and must be implemented in a specific order. Improper handling of these dependencies can delay project timelines, yet limited research addresses this challenge. This study aims to develop a systematic approach to prioritize requirements in order to minimize dependencies and improve the timely completion of the project. The Analytical Hierarchical Process (AHP) combined with spanning tree methodology was applied to analyze requirement dependencies. In addition, the NA technique was used to classify prioritized requirements into distinct categories. ODOO ERP requirements served as the case study for evaluation. The proposed methodology produced a prioritized list of requirements grouped into categories, which significantly reduced inter-dependencies and improved the organization of requirements. Minimizing requirement dependencies through structured prioritization enhances the reliability and timely completion of software development projects. In the ODOO ERP case study, the suggested approach reduced 90% of dependencies. Priority grouping showed that the top 25 requirements eliminated 90% of dependencies, while the top 20 and 15 removed 82% and 67% respectively. This reduction lowered the projected project delay rate from about 25% to under 5%, confirming the approach's practical effectiveness and scalability for large ERP projects.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Generating Quality Assurance Constraints From Natural Language With LLMs","authors":"Christoph Mayr-Dorn,&nbsp;Anmol Bilal,&nbsp;Cosmina Ratiu,&nbsp;Alexander Egyed","doi":"10.1002/smr.70062","DOIUrl":"https://doi.org/10.1002/smr.70062","url":null,"abstract":"<p>This paper addresses the challenge of automating process-centric quality assurance (QA) in safety-critical domains, where compliance with regulations is crucial. Currently, QA engineers manually check compliance using tedious methods like browsing engineering artifacts and ad-hoc scripts. Automated support could improve efficiency, but it requires constraints to be written in structured, executable forms (e.g., in the Object Constraint Language, OCL), whereas engineers prefer natural language. To bridge this gap, we propose the use of large language models (LLMs) to generate OCL from natural language, enhanced by schema-based prompting and domain-specific language (DSL)–based repairs. Unlike prior work focused on UML models, this work applies OCL to software process QA. Evaluating six LLMs, we find o1-mini and Codestral perform best, with our automatic repairs ensuring constraint executability for 22%–44% of an LLM's generated OCL constraints that would otherwise remain nonexecutable due to errors.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Utilizing Creator Profiles for Predicting Valuable User Enhancement Reports","authors":"Feifei Niu,&nbsp;Chuanyi Li,&nbsp;Heng Chen,&nbsp;Jidong Ge,&nbsp;Bin Luo,&nbsp;Alexander Egyed","doi":"10.1002/smr.70061","DOIUrl":"https://doi.org/10.1002/smr.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>Users of software applications use issue tracking systems (ITSs) to file enhancement reports, which leads to a large quantity of user requests. These reports play a pivotal role in shaping software requirements and continuous product improvement. However, the manual evaluation of these reports by developers and maintainers can be a time-consuming and labor-intensive process due to the constant influx of enhancement requests. Timely handling and implementation of these enhancement reports are crucial for enhancing user satisfaction and product competitiveness. In response to this challenge, research has concentrated on automated methods to predict which enhancement reports are likely to gain approval, aiming to maximize the value extracted from user feedback. Nevertheless, existing approaches still fall short in delivering practical results. In this paper, we introduce a novel creator profile-based approach designed to uncover the dependency between creators' identity and the value of enhancement reports, ultimately enhancing prediction accuracy. Firstly, we present the concept of a “creator profile” and outline a comprehensive methodology for generating creator profiles from the dataset. We then demonstrate how creator profiles can be effectively applied to the task of predicting the approval of enhancement reports. Subsequently, we assess the performance of our approach using a dataset of 40,551 enhancement reports collected from ITSs. The experimental results indicate a substantial improvement over the existing state of the art, particularly in predicting approved reports. For cross-application prediction, the accuracy reaches 80.7%, while for non–cross-application prediction, the overall accuracy is 83.6%. In essence, with the proposed approach, over 80% of user requests can be automatically identified for exacting valuable user requirements, which significantly reduces labor costs. The replication package is available at https://github.com/feifeiniu-se/approval_prediction.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"RM2EIS: Automatic Generation of Enterprise Information Systems From Contract-Based Requirements Model","authors":"Yilong Yang,&nbsp;Yihui Jian,&nbsp;Shaohong Zhu,&nbsp;Runkun Zhang,&nbsp;Zhi Li,&nbsp;Li Zhang","doi":"10.1002/smr.70051","DOIUrl":"https://doi.org/10.1002/smr.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>Enterprise Information System (EIS) streamlines business processes and enhances productivity by integrating various functions. However, conventional development methods are labor-intensive, time-consuming, and error-prone, often necessitating a design model from requirements for implementation. Existing solutions focus on auto-generating code from Object-Oriented (OO) design models, but specifying the design model from a validated requirements model requires more effort due to information gaps between requirements and design. This paper introduces RM2EIS, an approach that automatically generates EIS from contract-based requirements models, which include use case diagrams, conceptual class diagrams, and use case definitions specified by system sequence diagrams and contracts. System operation contracts are formally specified using pre- and post-conditions written in OCL. We conducted nine case studies to evaluate RM2EIS. The results indicate that the time of the generation including modeling and validation by RM2EIS is at least twice as fast as the design and implementation of developers. Moreover, the generated EIS outperforms the developer-implemented systems in functionality and is close to the non-functional aspects like performance.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Correction to “Key Success Factors of Cybersecurity Awareness in Distributed Teams”","authors":"","doi":"10.1002/smr.70065","DOIUrl":"https://doi.org/10.1002/smr.70065","url":null,"abstract":"<p>\u0000 <span>S. Algamdi</span>, <span>A. Khan</span>, <span>J. Ahmad</span>, and <span>M.-K. Ghembaza</span>, “ <span>Key Success Factors of Cybersecurity Awareness in Distributed Teams</span>,” <i>Journal of Software: Evolution and Process</i> <span>37</span>, no. <span>9</span> (<span>2025</span>): e70056, https://doi.org/10.1002/smr.70056.\u0000 </p><p>The project number in the funding section is incorrect. The correct funding details are shown below:</p><p>This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2024/01/31694).</p><p>We apologize for this error.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 11","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"The LAGSSE Framework: Integrating Lean, Agile, and Green Principles for Sustainable Software Engineering","authors":"Islam Zada,&nbsp;Sara Shahzad,&nbsp;Esraa Omran,&nbsp;Abdullah Alshahrani,&nbsp;Hessa Alfraihi,&nbsp;Yasir Hamid,&nbsp;Mohd Asif Shah","doi":"10.1002/smr.70058","DOIUrl":"https://doi.org/10.1002/smr.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>The demand for sustainable digital and information systems requires a transformative shift in software engineering practices that balance technical performance with economic, environmental, and social responsibility. This paper introduces LAGSSE, which is a novel framework that integrates Lean, Agile, and Green principles to advance Sustainable Software Engineering (SSE). LAGSSE operationalizes six interconnected sustainability dimensions: technical, environmental, economic, social, process, and infrastructure through a layered architecture and lifecycle-based approach, underpinned by the OntoSuSD ontology for semantic clarity and traceability. A two-phase validation strategy confirms the framework's strength; that is, a Delphi-based expert study (<i>n</i> = 75) establishes conceptual clarity and applicability, while simulation-based evaluations demonstrate measurable improvements in energy efficiency (25%), defect reduction (33%), deployment speed (30%), and stakeholder satisfaction (24%) compared to baseline Agile practices. Visual models, statistical charts, and a Sustainability Performance Score (SPS) equation reinforce the framework's empirical grounding and practical utility. This framework not only addresses critical gaps in existing SSE frameworks but also provides an adaptable, comprehensive, and validated pathway for embedding sustainability into software engineering education, industry practice, and policy development.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Comprehensive Framework for Testing Goal-Oriented NFPs in Software Product Lines","authors":"Ibtesam Bashir Gwasem","doi":"10.1002/smr.2760","DOIUrl":"https://doi.org/10.1002/smr.2760","url":null,"abstract":"<p>In the realm of software product line engineering (SPLE), ensuring the quality of end products is paramount for market success. SPLE promotes systematic software development through reuse by focusing on commonalities and variabilities within a domain to efficiently produce a family of related systems. The quality of a software system depends on its functional properties (FPs)—the functionalities it provides—and its non-functional properties (NFPs)—the quality attributes it possesses, such as security and performance. NFPs are particularly critical because they directly impact user satisfaction, determine project success, and significantly influence market acceptance. However, in SPLE, despite their recognized importance, NFPs often receive less attention compared to FPs, leading to potential quality risks and increased costs. This paper presents a framework for testing goal-oriented NFPs in software product lines, addressing this gap. By integrating goal models, the framework supports the systematic capture and validation of NFPs from early development stages. The framework's applicability is illustrated through research-based case studies in an online bookstore product line, demonstrating its use for systematic NFPs testing at both the domain and application levels. A comparative analysis with an existing technique highlights the framework's unique contributions in addressing NFPs testing within software product lines. Additionally, a preliminary experiment using two widely recognized product line domain examples evaluated the core testing process supported by the framework during the domain engineering phase, focusing on effectiveness, performance efficiency, and time consistency in structured research settings.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145316899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"PredictPP: A Rank-Based Weighted Ensemble Model for Prediction of Software Project Productivity","authors":"Suyash Shukla,&nbsp;Sandeep Kumar","doi":"10.1002/smr.70059","DOIUrl":"https://doi.org/10.1002/smr.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Software effort estimation (SEE) determines the effort necessary to develop software. The researchers have been tending to SEE issues since the 1960s, and several methods have been created until the formulation of the function point (FP) and constructive cost estimation (COCOMO) methods. However, these methods are only useful for procedurally developed software, not modern object-oriented (OO) software. Because the use case is the widely used unit of an OO system, particularly in scenarios requiring structured and early-stage effort estimation, using the use case point (UCP) approach will help get accurate results. The UCP approach consists of size estimation (in UCP) and effort estimation with calculated size. This study focuses on effort estimation when the size (in UCP) is already known. The productivity of a project is one of the main components for estimating effort from the given size. The classical SEE models based on UCP utilized a fixed number of productivity values. So, the validity of classical approaches is a subject of disapproval because of static productivity values. Purposefully, we proposed a rank-based weighted ensemble model for productivity prediction that allows us to use flexible productivity values. We used learning techniques such as simple linear regression (SLR), Least Absolute Shrinkage and Selection Operator Regression (LR), ridge regression (RR), elastic net regression (ER), K-nearest neighbor (KNN), decision tree (DT), support vector regression (SVR), multilayer perceptron (MLP), bagging, and adaptive boosting for productivity prediction and compared them with the proposed model. Further, we used existing UCP prediction models and compared the proposed approach with them.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Evaluation and Improvement of Test Selection for Large Language Models","authors":"Lili Quan,&nbsp;Jin Wen,&nbsp;Qiang Hu,&nbsp;Maxime Cordy,&nbsp;Yuheng Huang,&nbsp;Lei Ma,&nbsp;Xiaohong Li","doi":"10.1002/smr.70057","DOIUrl":"https://doi.org/10.1002/smr.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunately, many <i>faults</i> still exist that LLMs cannot properly predict. Such faults will harm the usability of LLMs in general and could introduce safety issues in reliability-critical systems such as autonomous driving systems. How to quickly reveal these faults in real-world datasets that LLMs could face is important but challenging. The major reason is that the ground truth is necessary but the data labeling process is heavy considering the time and human effort. To handle this problem, in the conventional deep learning testing field, test selection methods have been proposed for efficiently evaluating deep learning models by prioritizing faults. However, despite their importance, the usefulness of these methods on LLMs is unclear and underexplored. In this paper, we conduct the first empirical study to investigate the effectiveness of existing test selection methods for LLMs. We focus on classification tasks because most existing test selection methods target this setting and reliably estimating confidence scores for variable-length outputs in generative tasks remains challenging. Experimental results on four different tasks (including both code tasks and natural language processing tasks) and four LLMs (e.g., LLaMA3 and GPT-4) demonstrated that simple methods such as Margin perform well on LLMs, but there is still a big room for improvement. Based on the study, we further propose MuCS, a prompt Mutation-based prediction Confidence Smoothing framework to boost the test selection capability for LLMs specifically on classification tasks. Concretely, multiple prompt mutation techniques have been proposed to help collect diverse outputs for confidence smoothing. The results show that our proposed framework significantly enhances existing methods with test relative coverage improvement by up to 70.53%.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145272090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Validation Methodology for XAI Decision Support Systems Against Relational Domain Properties","authors":"Emanuele De Angelis,&nbsp;Guglielmo De Angelis,&nbsp;Maurizio Mongelli,&nbsp;Maurizio Proietti","doi":"10.1002/smr.70054","DOIUrl":"https://doi.org/10.1002/smr.70054","url":null,"abstract":"<p>The global adoption of artificial intelligence (AI) has increased dramatically in recent years, becoming commonplace in many fields. Such a pervasiveness has led to changes in how AI is perceived, strengthening discussions on its societal consequences. Thus, a new class of requirements for AI-based solutions emerged. Broadly speaking, those on “explainability” aim to provide a transparent representation of the (often opaque) reasoning method that an AI-based solution uses when prompted. This work presents a methodology for validating a class of explainable AI (XAI) models, called deterministic rule-based models, which are used for expressing an explainable approximation of classifiers based on machine learning. The validation methodology combines logical deduction with constraint-based reasoning in numerical domains, and it either succeeds or returns quantitative estimations of the invalid deviations found. This information allows us to assess the correctness of an XAI model, or in the case of deviations, to evaluate if it still can be deemed acceptable. The validation methodology has been applied to a simulation-based study where the decision-making process copes with the spread of SARS-COV-2 inside a railway station. The considered case study is a controlled but nontrivial example that shows the overall applicability of the methodology.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 10","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}