Information and Software Technology最新文献

{"title":"Navigating ASD barriers: a role-contextual framework for enhancing ASD implementation in self-organizing teams","authors":"Soumya Prakash Rath ,&nbsp;Gunjan Tomer","doi":"10.1016/j.infsof.2025.107997","DOIUrl":"10.1016/j.infsof.2025.107997","url":null,"abstract":"<div><h3>Context</h3><div>Agile Software Development (ASD) emphasizes adaptability, collaboration, and rapid, iterative delivery to address evolving client needs and manage interdependent roles effectively. Though extant literature has explored ASD and its various dimensions, intersections, and dependencies among various well-defined ASD job roles, it remains underexplored.</div></div><div><h3>Objective</h3><div>This study examines the implementation barriers faced by ASD teams and identifies the best practices adopted across key ASD roles—Scrum Masters, Product Owners, and Developers—as well as senior leadership, to overcome them.</div></div><div><h3>Method</h3><div>This study employs a Grounded Theory (GT) methodology to explore the complex, role-based challenges in ASD inductively. Drawing on qualitative insights from 23 industry practitioners representing multiple ASD roles, the research delves into the ground realities of ASD projects to surface emergent patterns and contextual nuances. Through iterative coding and constant comparison, the study develops a role-contextual understanding of barriers to ASD implementation and synthesizes practitioner-validated best practices for overcoming them.</div></div><div><h3>Results</h3><div>The study identifies a spectrum of barriers—role-specific, inter-role, and leadership-related—spanning strategic, operational, and interpersonal dimensions. It proposes a Role-Contextual Framework that categorizes these barriers at triadic, dyadic, and intra-role levels and links them to practitioner-validated best practices.</div></div><div><h3>Conclusion</h3><div>The findings contribute to ASD research by highlighting the contextual and systemic nature of ASD barriers and underscoring the need for adaptive, role-based strategies, such as strategic alignment and leadership involvement, to facilitate the successful implementation of ASD.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107997"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797614","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":"Balancing organizational alignment, team autonomy, and control in large-scale agile organizations","authors":"Bart Daemen,&nbsp;Konstantinos Tsilionis,&nbsp;Oktay Turetken","doi":"10.1016/j.infsof.2025.107998","DOIUrl":"10.1016/j.infsof.2025.107998","url":null,"abstract":"<div><h3>Context</h3><div>Agile software development has become widespread, particularly in small team settings, because of its emphasis on user value and adaptability. Scaling agile practices in large organizations, however, presents challenges. As organizations grow, projects become complex, requiring control mechanisms to ensure quality deliverables, align outputs across teams, and connect them to organizational goals. Scaling agile frameworks provide structured approaches to coordinate teams and maintain alignment while preserving team autonomy. Yet, applying these frameworks rigidly can reduce flexibility, overlook organizational context, and disrupt the balance between autonomy and alignment.</div></div><div><h3>Objective</h3><div>This study examines how different control mechanisms, both formal controls such as structured processes, rules, and performance metrics, and informal controls such as trust, social norms, and mutual adjustment, can foster organizational alignment while preserving team autonomy in large organizations that implement scaling agile frameworks.</div></div><div><h3>Methods</h3><div>A multi-method approach was adopted, combining a systematic literature review with an interpretive case study. The literature review synthesized prior research and produced eight theoretical propositions on the dynamic interaction of control, alignment, and autonomy. The case study was conducted in the IT department of a multinational organization transitioning from waterfall to agile, offering an empirical setting to explore how these propositions unfold in practice.</div></div><div><h3>Results</h3><div>Our findings show that formal controls, such as adherence to architectural standards, provide structure and support alignment. Informal mechanisms, including communities of practice, peer coaching, and visibility-based incentives, enhance accountability and autonomy. However, rigid separation of formal and informal controls is restrictive. More integrated hybrid approaches that combine agile methods with structured processes, supported by parallel decision-making bodies, address inter-team dependencies while maintaining flexibility.</div></div><div><h3>Conclusion</h3><div>Large organizations should adopt context-sensitive strategies when applying scaling agile frameworks. Control mechanisms need to be tailored to organizational needs, maturity, and conditions, supported by clear communication, cultural commitment, and adaptive governance.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107998"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797613","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":"Compositional security analysis of dynamic component-based systems","authors":"Charilaos Skandylas ,&nbsp;Narges Khakpour","doi":"10.1016/j.infsof.2025.108002","DOIUrl":"10.1016/j.infsof.2025.108002","url":null,"abstract":"<div><h3>Context:</h3><div>To reason about and enforce security in dynamic software systems, automated analysis and verification approaches are required. However, such approaches often encounter scalability issues, particularly when employed for runtime analysis, which is necessary in software systems with dynamically changing architectures, such as self-adaptive systems.</div></div><div><h3>Objective:</h3><div>In this work, we propose an automated formal approach for security analysis of component-based systems with dynamic architectures.</div></div><div><h3>Methods:</h3><div>This approach leverages formal abstraction and incremental analysis techniques to reduce the complexity of runtime analysis. We have implemented and evaluated our approach against ZNN, a widely known self-adaptive system exemplar.</div></div><div><h3>Results:</h3><div>Compared to the state of the art, our results demonstrate an improvement both in the size of systems that can be analyzed and at the time required to complete the analysis. In particular, our incremental analysis is well suited for systems that alter their architectures at runtime.</div></div><div><h3>Conclusion:</h3><div>Therefore, this approach is suitable for analyzing the security dynamic component based both statically and at runtime.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 108002"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884035","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":"Towards green game software engineering: A comparative analysis of energy consumption between the widespread unity and unreal video game engines","authors":"Carlos Pérez ,&nbsp;Javier Verón ,&nbsp;Francisca Pérez ,&nbsp;MaÁngeles Moraga ,&nbsp;Coral Calero ,&nbsp;Carlos Cetina","doi":"10.1016/j.infsof.2025.107991","DOIUrl":"10.1016/j.infsof.2025.107991","url":null,"abstract":"<div><h3>Context:</h3><div>The total energy cost of computing activities is steadily increasing, and projections indicate that it will be one of the dominant global energy consumers in the coming decades. However, the video game sector has not yet developed the same level of environmental awareness as other computing technologies despite the estimated three billion regular video game players in the world.</div></div><div><h3>Objective:</h3><div>This work evaluates the energy consumption of the most widely used industry-scale video game engines: Unity and Unreal Engine.</div></div><div><h3>Method:</h3><div>Specifically, our work uses three scenarios representing relevant aspects of video games (Physics, Static Meshes, and Dynamic Meshes) to compare the energy consumption of the engines. The aim is to determine the influence of using each engine on energy consumption.</div></div><div><h3>Results:</h3><div>Our research has confirmed notable differences in energy consumption: 351% in Physics in favor of Unity, 17% in Static Meshes in favor of Unity, and 26% in Dynamic Meshes in favor of Unreal Engine.</div></div><div><h3>Conclusion:</h3><div>Considering the estimated three billion regular video game players worldwide and the high computational requirements of the sector, the magnitude of potential savings is a relevant issue for the research community. This might encourage a new branch of research on energy efficient video game engines.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107991"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694060","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":"Quality assessment of software requirements using artificial intelligence methods: A systematic literature review","authors":"Elise Wolf,&nbsp;Adam Trendowicz,&nbsp;Julien Siebert","doi":"10.1016/j.infsof.2025.107979","DOIUrl":"10.1016/j.infsof.2025.107979","url":null,"abstract":"<div><h3>Context:</h3><div>The quality of requirements specifications is a critical success factor in software development. Assuring high-quality requirements, specifically in an automated way, poses a significant challenge due to their unstructured and multi-modal character. With the rise of deep learning and large language models (LLMs), new opportunities have developed to assess the quality of requirements automatically, particularly user stories in the context of agile software engineering, where short development cycles require efficient tool support.</div></div><div><h3>Objective:</h3><div>This study aims to systematically review and investigate the current landscape of approaches based on artificial intelligence techniques such as natural language processing and deep learning for assessing the quality of software requirements. The investigation focuses on the artificial intelligence techniques adopted, quality aspects considered, datasets used to tune and evaluate the proposed approaches, and their performance.</div></div><div><h3>Method:</h3><div>We conducted a systematic literature review of 26 peer-reviewed papers published between 2019 and 2025. We selected the papers after a title and abstract review of 353 papers identified through a literature databases query and forward–backward snowballing.</div></div><div><h3>Results:</h3><div>The results reveal significant overlap among considered quality aspects, which can be mapped onto the higher-order requirements quality model INVEST. Most studies focus on assessing requirement quality rather than improving requirements and rely heavily on synthetic and public datasets. LLMs have rapidly gained popularity since 2023, though model evaluation strategies remain inconsistent. Metrics such as accuracy, precision, recall, and F1-Score are common, yet a few studies use semantic or expert-based evaluations.</div></div><div><h3>Conclusion:</h3><div>The field is evolving toward LLM-driven, semantically rich models, yet lacks methodological standardization, reproducible datasets for evaluating the models, and integration of the approaches with real-world requirements engineering processes. Future work should address these limitations by developing benchmark datasets, standardizing evaluation metrics, and exploring hybrid systems that combine AI-based and traditional requirements quality assurance approaches.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107979"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694058","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":"ReEPM: A Reliability Estimation Framework for CNNs based on Error Probability Matrix modeling","authors":"Jie Xiao ,&nbsp;Aizhu Liu ,&nbsp;Yujian Yang ,&nbsp;Yuhao Huang ,&nbsp;Zhezhao Yang ,&nbsp;Jungang Lou","doi":"10.1016/j.infsof.2025.107981","DOIUrl":"10.1016/j.infsof.2025.107981","url":null,"abstract":"<div><h3>Context:</h3><div>The deployment of Convolutional Neural Networks (CNNs) in safety-critical applications faces significant challenges from soft errors. While accurate reliability assessment is vital, existing methods typically suffer from prohibitively high computational overheads, creating a critical trade-off between precision and efficiency that severely limits their practical applicability.</div></div><div><h3>Objective:</h3><div>To overcome this critical precision-efficiency dilemma, we aim to design a novel framework designed for CNN reliability assessment, aiming for accurate and highly efficient CNN reliability evaluation across diverse error conditions.</div></div><div><h3>Method:</h3><div>We propose ReEPM (Reliability Estimation Framework based on Error Probability Matrix). ReEPM constructs an Error Probability Matrix (EPM) that precisely models bit-flip error impact on CNN weights, fundamentally enabling parallel, accurate error injection without brute-force simulation. Moreover, we integrate an adaptive iterative process driven by Kalman filtering, which intelligently converges on reliability estimates with a drastically reduced number of input samples. This combination offers superior analytical rigor and computational efficiency.</div></div><div><h3>Result:</h3><div>Experimental results show that ReEPM achieves average accuracy of 0.9017 (single-error) and 0.9984 (multiple-error), while being 69.53<span><math><mo>×</mo></math></span> and 1989.27<span><math><mo>×</mo></math></span> faster compared widely adopted Monte Carlo fault injection, respectively. Furthermore, ReEPM significantly outperforms probability-based methods like SERN in accuracy (0.9017 vs. 0.7192 in single-error) and boasts broader applicability, covering entire networks and complex multiple-error scenarios.</div></div><div><h3>Conclusion:</h3><div>ReEPM establishes a new paradigm for CNN reliability assessment by effectively overcoming the critical accuracy-overhead trade-off. It offers an accuracy and rapid evaluation tool for designing resilient CNNs in next-generation safety-critical intelligent systems.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107981"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694063","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":"Representation learning for coincidental correctness in fault localization","authors":"Jian Hu","doi":"10.1016/j.infsof.2025.107978","DOIUrl":"10.1016/j.infsof.2025.107978","url":null,"abstract":"<div><h3>Context:</h3><div>Fault localization (FL) is a critical phase in the software debugging process, which employs execution coverage matrix to identify the exact locations of faults or bugs in a program’s source code. However, researchers have proved that coincidental correctness test cases (CCTC) that execute the faulty statements whereas produce the correct output are prevalent in test suites and can negatively affect the effectiveness of fault localization.</div></div><div><h3>Objective:</h3><div>To address this problem, we propose ER4FL: a representation learning based CCTC detection method for fault localization. Our method first detects the CCTCs in the coverage matrix, then relabels them, and finally uses the optimized coverage matrix for fault localization.</div></div><div><h3>Method:</h3><div>ER4FL leverages an autoencoder-based representation learning to refine the coverage matrix, which captures its most important features in a compressed form. Based on the enhanced representation (i.e., compact coverage matrix), ER4FL adopts a Gaussian Mixture Model (GMM) as a probabilistic model to identify and manipulate CCTC. Finally, ER4FL fed the coverage matrix without CCTC into FL pipeline.</div></div><div><h3>Results:</h3><div>Our experimental results demonstrate that ER4FL reduces the Mean First Rank (MFR) of Ochiai from 333.18 to 258.26, achieving a relative improvement of 22.49%. In addition, ER4FL decreases the number of checked statements in Convolutional Neural Network (CNN) FL from 859.20 to 579.65, corresponding to a relative reduction of 48.23%.</div></div><div><h3>Conclusion:</h3><div>The experimental results demonstrate that our method is statistically more effective than the six FL baselines, as well as the two CCTC detection methods.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107978"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145584367","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":"Maximizing quantum hardware utilization via multiprogramming circuits and shot-wise distribution","authors":"Giuseppe Bisicchia ,&nbsp;Jaime Alvarado-Valiente ,&nbsp;Javier Romero-Álvarez ,&nbsp;Jose Garcia-Alonso ,&nbsp;Juan M. Murillo ,&nbsp;Antonio Brogi","doi":"10.1016/j.infsof.2025.108005","DOIUrl":"10.1016/j.infsof.2025.108005","url":null,"abstract":"<div><h3>Context:</h3><div>Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era.</div></div><div><h3>Objective:</h3><div>This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline.</div></div><div><h3>Methods:</h3><div>An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits.</div></div><div><h3>Results:</h3><div>The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances.</div></div><div><h3>Conclusions:</h3><div>This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 108005"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840127","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":"DeepRegion: Black-box efficient testing for DNNs based on region analysis","authors":"Qing Sheng ,&nbsp;Zhiyi Zhang ,&nbsp;Shuxian Chen ,&nbsp;Ziyuan Wang ,&nbsp;Zhiqiu Huang","doi":"10.1016/j.infsof.2025.107995","DOIUrl":"10.1016/j.infsof.2025.107995","url":null,"abstract":"<div><h3>Context:</h3><div>Deep Neural Networks (DNNs) have achieved remarkable success in many safety-critical domains. However, their vulnerability to small input perturbations poses serious security risks. White-box testing methods are often impractical in sensitive domains where access to model internals is restricted. Moreover, some recent studies have shown that neuron coverage does not have a strong correlation with the ability to detect faults, making these methods relatively inefficient. Black-box testing offers a viable alternative by evaluating models solely through input–output behavior. But current approaches typically apply uniform perturbations across the input space, overlooking the fact that DNN decisions often rely heavily on a small set of critical input regions, making them suffer from low efficiency and high query costs.</div></div><div><h3>Objective:</h3><div>To improve query efficiency in black-box testing of DNNs and reduce computational costs while enhancing defect detection within limited query budgets, this paper introduces DeepRegion, a novel black-box testing method based on region analysis.</div></div><div><h3>Method:</h3><div>First, we design a partition policy to initialize the partitioning of the original image. In each iteration, we apply a well-designed discriminant function to guide the localization process of the region, and apply the dynamic adjustment process in the partition policy to refine the partition for further analysis. After the regions containing important features are successfully located, we then implement a feedback-based approach to strategically perturb these regions, generating error-inducing inputs.</div></div><div><h3>Results:</h3><div>Our experimental evaluation on four widely used datasets and seven well-established DNN architectures shows that DeepRegion can not only improve testing efficiency but also enhance defect detection within limited query budgets.</div></div><div><h3>Conclusion:</h3><div>DeepRegion is capable of automatically generating high-quality test inputs to detect the inconsistency of the target systems under the condition of limited resources, and the error-inducing inputs found by DeepRegion can be used to fine-tune target models to improve accuracy.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107995"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748130","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":"Toward an automated cross-multimodal verification of mobile app bug fixes integrating user feedback, developer responses, changelogs, and UI visual analysis","authors":"Rhodes Massenon ,&nbsp;Ishaya Gambo ,&nbsp;Javed Ali Khan","doi":"10.1016/j.infsof.2025.107996","DOIUrl":"10.1016/j.infsof.2025.107996","url":null,"abstract":"<div><h3>Context</h3><div>Verifying claimed bug fixes in mobile applications is crucial, yet the \"fixed but not resolved\" phenomenon remains a persistent challenge. Existing bug analysis tools focus on pre-fix tasks like detection and reproduction, but lack mechanisms to holistically verify a fix post-deployment by cross-referencing developer claims, visual UI changes, and subsequent user feedback. This gap leads to persistent bugs, wasted developer effort, and user dissatisfaction.</div></div><div><h3>Objective</h3><div>This paper introduces BUGFixChecker, the first framework for automated, multimodal cross-verification of mobile app bug fixes. Our primary goal is to determine if a claimed fix has truly resolved a user-reported issue.</div></div><div><h3>Methods</h3><div>BUGFixChecker integrates five data sources: the original user bug report, the developer's fix claim, \"before\" and \"after\" UI screenshots, and post-fix user reviews. The core methodology employs a Multimodal Large Language Model (MLLM) guided by a Chain-of-Thought prompt to perform a comparative reasoning task. We evaluated the framework on a curated dataset of 53 real-world bug fix cases from Android applications.</div></div><div><h3>Results</h3><div>BUGFixChecker achieved a high overall accuracy of 83.0 % and a macro F1-score of 0.805 in correctly verifying the status of bug fixes. It proved particularly effective at identifying discrepancies with strong evidentiary signals, such as \"Unresolved Visual Mismatch\" (F1-score = 0.865). Most significantly, a rigorouss ablation study demonstrated the critical contribution of the visual modality: the full multimodal framework outperformed a text-only baseline by over 19 % points in F1-score (0.805 vs. 0.610), proving that visual evidence is indispensable for this task.</div></div><div><h3>Conclusion</h3><div>BUGFixChecker offers a novel and pragmatic approach to automated bug fix verification. By moving beyond pre-fix analysis to the critical post-fix verification stage, our multimodal framework provides a scalable solution to enhance the integrity of bug tracking systems, reduce developer workload, and ensure higher software quality in rapidly evolving mobile ecosystems.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"191 ","pages":"Article 107996"},"PeriodicalIF":4.3,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797611","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}