Journal of Systems and Software最新文献

{"title":"Systematic literature review on software code smell detection approaches","authors":"Praveen Singh Thakur ,&nbsp;Satyendra Singh Chouhan ,&nbsp;Santosh Singh Rathore ,&nbsp;Jitendra Parmar","doi":"10.1016/j.jss.2026.112784","DOIUrl":"10.1016/j.jss.2026.112784","url":null,"abstract":"<div><div>Software code smells, subtle indicators of potential design flaws, play a critical role in maintaining software quality and preventing future maintenance issues. Numerous researchers have proposed various tools and employed different machine learning and deep learning techniques to detect software code smells. This survey systematically reviews the work conducted on detecting software code smells through tool-based, ML-based, and DL-based approaches published from 2014 to 2024. The imbalanced nature of datasets is another vital issue in this domain, where instances of software code smells are often significantly underrepresented, which poses a substantial challenge for traditional detection techniques. Therefore, this review also includes efforts to detect software code smells using different imbalance learning techniques. After initial scrutiny and selection, a total of 86 studies are analyzed and reported in this review work, providing a comprehensive overview of the field. This work comprehensively analyzes the intersection between software code smell detection and imbalance learning techniques, highlighting challenges posed by imbalanced datasets. Furthermore, we identify the best-performing ML techniques (e.g., Random Forest, SVM), the most commonly detected code smells (e.g., God Class, Data Class, Long Method, and feature Envy), and popular experimental setup techniques (e.g., K-fold cross-validation) used in prior studies. Based on the analysis, several key challenges and research gaps are identified, offering directions for future research.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"235 ","pages":"Article 112784"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038342","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":"Enabling direct manipulation of plain-text output for template programs","authors":"Tao Zan ,&nbsp;Xiao He ,&nbsp;Zhenjiang Hu","doi":"10.1016/j.jss.2025.112754","DOIUrl":"10.1016/j.jss.2025.112754","url":null,"abstract":"<div><div>In modern software development, templates are widely used to generate artifacts, enhancing productivity and consistency. However, developers often face a tedious run-modify-run loop to verify template correctness, as outputs are only inspectable after execution. This issue is exacerbated when templates involve complex logic, highlighting the need for a solution that supports both artifact generation and user-friendly modifications. We propose <span>BIT2</span>, a framework enabling direct manipulation of plain-text output to modify template programs. A key challenge is that plain-text output lack structural information, making it difficult to map updates back to the template. <span>BIT2</span> addresses this by generating computation-structured output, preserving the underlying logic, and supporting update operations (insertion, replacement, deletion) with carefully designed fusion semantics. Our implementation, written in TypeScript (18,000 lines of code), is available as an NPM module, complemented by a VSCode plugin, LiveT, for seamless template editing. Experimental results demonstrate that <span>BIT2</span> achieves efficient evaluation and supports diverse update scenarios, significantly enhancing expressiveness in template development.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"235 ","pages":"Article 112754"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928352","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":"Inter-organizational collaborations in open-source software ecosystems","authors":"Roland Robert Schreiber ,&nbsp;Thomas Wieland","doi":"10.1016/j.jss.2025.112765","DOIUrl":"10.1016/j.jss.2025.112765","url":null,"abstract":"<div><h3>Context</h3><div>Open-source software (OSS) ecosystems are pivotal to modern software development by fostering innovation through inter-organizational collaboration. While becoming increasingly important, research on the patterns and dynamics of this collaboration in OSS remains limited.</div></div><div><h3>Objective</h3><div>This study investigates inter-organizational collaboration within different OSS ecosystems, focusing on identifying key contributing and influential organizations, analyzing collaboration patterns, and understanding their evolution over time.</div></div><div><h3>Method</h3><div>An exploratory case study with a mixed-methods approach was conducted, with data collected from five prominent OSS ecosystems on GitHub (React, Vue, TensorFlow, Bootstrap, and Flutter) involving 9947 developers and 339 organizations over ten years. Both qualitative and quantitative analyses were performed, including software repository mining and applying different levels (micro/macro) and dimensions (rational/structural/functional) of social network analysis (SNA) to examine collaboration patterns.</div></div><div><h3>Results</h3><div>Key organizations, such as Alphabet, TensorFlow, Meta, Nvidia, Intel, MobileIron, IBM, and AMD, emerged as significant contributors, acting as central hubs for collaboration within OSS ecosystems. The collaboration networks showed an initial phase of rapid growth followed by stabilization, indicating project maturation. Notably, competing firms were found to contribute to the same ecosystems, underpinning the inherently cooperative, reciprocal, and multiplex nature of OSS development.</div></div><div><h3>Conclusion</h3><div>The findings highlight the pivotal role of top-contributing organizations in fostering collaboration and driving innovation within OSS ecosystems. Understanding these dynamics provides valuable insights for developing strategies to enhance the effectiveness, scalability, and long-term sustainability of OSS ecosystems, benefiting both individual contributors and participating organizations.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"235 ","pages":"Article 112765"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886521","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":"Technical debt is not just technical: An industrial case study in large agile software development","authors":"Muhammad Ovais Ahmad ,&nbsp;Vladimir Mandić ,&nbsp;Nebojša Taušan ,&nbsp;Andrej Katin ,&nbsp;Pavithra Herath","doi":"10.1016/j.jss.2025.112719","DOIUrl":"10.1016/j.jss.2025.112719","url":null,"abstract":"<div><div>Software organizations of all sizes are affected by the Technical debt phenomenon. Large organizations, however, are more prone to their non-technical aspects due to a large number of teams of different sizes, heterogeneity of expertise, and the need for continuous management and communication. This is even more emphasized in large agile software development contexts due to a focus on personal interactions and team collaboration, which consequently poses a challenge due to a significant increase in communication pathways. The goal of this study is to investigate non-technical aspects of technical debt in the context of large-scale agile software development. To achieve this objective, a case study involving four international companies was conducted. During the study, 24 experts were interviewed, and their responses were analyzed using a qualitative research approach. The analysis resulted in five non-technical aspects, which are: social dynamics, process, people, documentation, and requirements aspects, and their indicators, which are specific in a large-scale agile context. The study findings suggest that lack of communication, collaboration, and cooperation are the key contributors to identified debt aspects, technical debt accumulation through these non-technical aspects, and that many of the causes stem from a culture of not developing rules, protocols, or guidelines.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112719"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685642","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":"On the use of unsupervised machine learning for classification of crowd-based software requirements","authors":"Naimish Sharma,&nbsp;Arpit Sharma","doi":"10.1016/j.jss.2025.112724","DOIUrl":"10.1016/j.jss.2025.112724","url":null,"abstract":"<div><div>Crowd-based requirements engineering (CrowdRE) involves large-scale user participation to gather software requirements. Supervised machine learning (SML) is commonly used to classify these requirements but demands significant time, computational resources, and high-quality labeled data, which are scarce in CrowdRE. The objective of this paper is to investigate the potential of clustering-based unsupervised ML to classify crowd-based requirements. Our framework evaluates sentence embedding models which convert textual requirements into numerical vectors, selecting optimal ones using information retrieval (IR) measures. These vectors are grouped via clustering algorithms, followed by manual or automated label assignment. Automated labeling involves generating a class-specific corpus for every class and computing semantic similarity to assign labels, while manual labeling is supported by topic modeling which uncovers thematic structures within every cluster. We validated the framework on 3000 crowd-generated smart home requirements, tackling binary, tertiary, quaternary, and quinary classification tasks. Automated labeling achieved F1 scores of up to  ∼ 90 %,  ∼ 82 %,  ∼ 70 %, and  ∼ 52 %, respectively, with manual labeling showing similar performance. Compared to logistic regression which is a supervised ML model, our framework occasionally outperformed it in F1 scores. Against Llama-3.2-3B-Instruct, which is a state-of-the-art lightweight large language model (LLM), it surpassed performance in 38 % of automated and 35 % of manual labeling cases. We also show that our framework enables one to analyze and identify labeling-related issues in the dataset, enhancing ground-truth data quality. These findings show that computationally efficient unsupervised methods effectively classify software requirements in data-scarce CrowdRE settings, offering a viable alternative to supervised approaches.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112724"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840847","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":"Exploring quality aspects of customer self-service in IT service provision: A case study","authors":"Marko Jäntti ,&nbsp;Henri Lindström","doi":"10.1016/j.jss.2025.112725","DOIUrl":"10.1016/j.jss.2025.112725","url":null,"abstract":"<div><div>Customer self-service (CSS) plays a critical role in IT service provider organizations’ service business. Self-service can remarkably reduce the volume of IT support tickets, such as service requests and incidents, and provides customers and service users with 24/7 availability to access IT services. Self-service channels can also be used to offer solutions to service-related problems and answers to common issues. Modern customer self-service is implemented in practice by aggregating various self-service technologies and service management practices together. While technologies evolve, this also affects the concept of quality in the context of customer self-service. The purpose of the research is to explore quality aspects related to modern IT customer support, especially a customer self-service portal (SSP). The research problem of the study is: How quality of customer self-service can be improved in the context of IT service provision? This paper presents a unique case study addressing the quality of modern self-service technologies. The cross-case synthesis from four IT self-service portal deployment cases was used to derive a novel multi-dimensional self-service quality model with three dimensions: technology, management and organization.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112725"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790960","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":"Serializing java objects in plain code","authors":"Julian Wachter ,&nbsp;Deepika Tiwari ,&nbsp;Martin Monperrus ,&nbsp;Benoit Baudry","doi":"10.1016/j.jss.2025.112721","DOIUrl":"10.1016/j.jss.2025.112721","url":null,"abstract":"<div><div>In managed languages, serialization of objects is typically done in bespoke binary formats such as Protobuf, or markup languages such as XML or JSON. The major limitation of these formats is readability. Human developers cannot read binary code, and in most cases, suffer from the syntax of XML or JSON. This is a major issue when objects are meant to be embedded and read in source code, such as in test cases.</div><div>To address this problem, we propose plain-code serialization. Our core idea is to serialize objects observed at runtime in the native syntax of a programming language. We realize this vision in the context of Java, and demonstrate a prototype which serializes Java objects to Java source code. The resulting source faithfully reconstructs the objects seen at runtime. Our prototype is called <span>ProDJ</span>and is publicly available. We experiment with <span>ProDJ</span>to successfully plain-code serialize 174,699 objects observed during the execution of 4 open-source Java applications. Our performance measurement shows that the performance impact is not noticeable. Through a user study, we demonstrate that developers prefer plain-code serialized objects within automatically generated tests over their representations as XML or JSON.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112721"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790962","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":"CARE: Clinical AI predictor for posterior urethal valves - design, explainability and evaluation","authors":"Roberta De Fazio ,&nbsp;Stefano Marrone ,&nbsp;Paola Tirelli ,&nbsp;Raffaele Chianese ,&nbsp;Clelia Di Nardo ,&nbsp;Pierluigi Marzuillo ,&nbsp;Laura Verde","doi":"10.1016/j.jss.2025.112720","DOIUrl":"10.1016/j.jss.2025.112720","url":null,"abstract":"<div><div>In the last decades, the remarkable impact achieved by Artificial Intelligence (AI) in business and industry has not been mirrored in critical real-world applications. The industrial diffusion of AI in healthcare is facing some resistance due to the lack of uniform legal frameworks and general scepticism among society and medical personnel. This paper proposes a multidisciplinary approach to fill the gap between the theoretical AI-based framework and real clinical practice, tailored to the problem of Posterior Urethral Valves (PUVs) diagnosis in paediatric patients. The multidisciplinary core of the work allows tackling the problem not only under the technical lens, but also from a clinical and industrial perspective: through the adoption of classifier composition mechanisms, this study presents the lessons learned in developing a reliable PUV classifier, as well as in its empirical assessment against real-world data and within a structured diagnostic process. The main contribution of this study is the design of a clinical decision support system for medical experts, which evaluates the behaviour of the model clinically and validates the extracted rules using explainability techniques on real-world data. AI classifiers leveraging vertical training of specialised models were adopted, achieving an overall accuracy of 70 %.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112720"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738079","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 search-based file recommendation approach for infrastructure-as-code evolution","authors":"Narjes Bessghaier ,&nbsp;Ali Ouni ,&nbsp;Mohammed Sayagh ,&nbsp;Mohamed Wiem Mkaouer","doi":"10.1016/j.jss.2025.112746","DOIUrl":"10.1016/j.jss.2025.112746","url":null,"abstract":"<div><div>Infrastructure-as-Code (IaC) is increasingly adopted in modern software projects to automate the setup of infrastructure components like servers and networking through code-based files. However, as configurations grow in size, complexity, and coupling with other artifacts (e.g., test files), it becomes challenging for development teams to identify the right files to change. Therefore, we propose an automated approach to recommend files likely to co-change with a given IaC file. Our approach uses a mono-objective genetic algorithm (GA) with a combination of two heuristics: file <em>similarity</em> and change <em>history</em>. Then, GA searches for the optimal solution and generates a ranked list of files from the most to the least likely files to co-change with the given IaC file. We evaluated our approach on 20 open-source Ansible and Puppet projects. Results show that our approach correctly recommended files in 86 % of commits within the top 10 recommendations. Using Instance Space Analysis (ISA), we found that our approach performs better for IaC files relying heavily on external modules and maintained by dedicated developers. However, GA struggles with highly customized Ansible files. In Puppet projects, high path similarity did not consistently predict co-changing files, while higher content similarity improved the similarity heuristic.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112746"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840749","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":"HiFlaky: Hierarchy-aware flakiness classification","authors":"Zheyuan Li ,&nbsp;Zhenyu Wu ,&nbsp;Yan Lei ,&nbsp;Huan Xie ,&nbsp;Maojin Li ,&nbsp;Jian Hu","doi":"10.1016/j.jss.2025.112741","DOIUrl":"10.1016/j.jss.2025.112741","url":null,"abstract":"<div><div>Flaky tests present a major challenge in software testing, as they intermittently pass or fail without changes to the source code, leading to developer frustration and wasted resources. Current methods for detecting and classifying flaky tests often overlook the hierarchical dependencies among the root causes of flakiness in test code. Additionally, their tools fail to handle complex cases involving multiple flaky causes and lack the capability for fine-grained classification.</div><div>To this end, we propose HiFlaky , a hierarchy-aware multi-label classification method for flaky tests that identifies multiple co-occurring root causes along different hierarchical paths, such as diagnosing a single test with both “Flaky/NOD/Network” and “Flaky/NOD/Concurrency/Async Wait”. HiFlaky leverages static semantic features from test code and hierarchy features, without dependency on dynamic execution features. To overcome the shortage of multi-root annotated data, we build a new dataset by expanding and labeling existing single-root datasets (IDoFT and FlakeFlagger) and 335 flaky tests from 145 Java projects on GitHub. Empirical evaluations demonstrate the effectiveness of HiFlaky in addressing both single and multi-root cause scenarios. In single-root cause scenarios, HiFlaky exhibits enhanced prediction accuracy compared to state-of-the-art methods, achieving a 30 % increase in Precision and an F1 score of 79 %. Furthermore, the fine-grained classification offered by HiFlaky provides useful insights that can facilitate root cause analysis, reduce debugging efforts, and contribute to improved software reliability. For complex multi-root cause scenarios, HiFlaky attains a Micro-F1 score of 0.812 and a Macro-F1 score of 0.414 across 29 categories.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"234 ","pages":"Article 112741"},"PeriodicalIF":4.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737972","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}