Journal of Software-Evolution and Process最新文献

{"title":"A Process Model for AI-Enabled Software Development: A Synthesis From Validation Studies in White Literature","authors":"Tugba Gurgen Erdogan,&nbsp;Haluk Altunel,&nbsp;Ayça Kolukısa Tarhan","doi":"10.1002/smr.2743","DOIUrl":"https://doi.org/10.1002/smr.2743","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Context</h3>\u0000 \u0000 <p>With the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>With an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI-enabled software system development.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>To the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom-up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116618","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":"AI-Augmented Software Engineering: Revolutionizing or Challenging Software Quality and Testing?","authors":"Tafline Ramos,&nbsp;Amanda Dean,&nbsp;David McGregor","doi":"10.1002/smr.2741","DOIUrl":"https://doi.org/10.1002/smr.2741","url":null,"abstract":"<div>\u0000 \u0000 <p>With organizations seeking faster, cheaper, and smarter ways of delivering higher quality software, many are looking towards generative artificial intelligence (AI) to drive efficiencies and innovation throughout the software development lifecycle. However, generative AI can suffer from several fundamental issues, including a lack of traceability in concept generation and decision-making, the potential for making incorrect inferences (hallucinations), shortcomings in response quality, and bias. Quality engineering (QE) has long been utilized to enable more efficient and effective delivery of higher quality software. A core aspect of QE is adopting quality models to support various lifecycle practices, including requirements definition, quality risk assessments, and testing. In this position paper, we introduce the application of QE to AI systems, consider shortcomings in existing AI quality models from the International Organization for Standardization (ISO), and propose extensions to ISO models based on the results of a survey. We also reflect on skills that IT graduates may need in the future, to support delivery of better-quality AI.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252905","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":"Software Refactoring Network: An Improved Software Refactoring Prediction Framework Using Hybrid Networking-Based Deep Learning Approach","authors":"T. Pandiyavathi,&nbsp;B. Sivakumar","doi":"10.1002/smr.2734","DOIUrl":"https://doi.org/10.1002/smr.2734","url":null,"abstract":"<p>Software refactoring plays a vital role in maintaining and improving the quality of software systems. The software refactoring network aims to connect developers, researchers, and practitioners to share knowledge, best practices, and tools related to refactoring. However, the network faces various challenges, such as the complexity of software systems, the diversity of refactoring techniques, and the need for automated and intelligent solutions to assist developers in making refactoring decisions. By leveraging deep learning techniques, the software refactoring network can enhance the speed, accuracy, and relevance of refactoring suggestions, ultimately improving the overall quality and maintainability of software systems. So, in this paper, an advanced deep learning–based software refactoring framework is proposed. The suggested model performs three phases as (a) data collection, (b) feature extraction, and (c) prediction of software refactoring. Initially, the data is collected from ordinary datasets. Then, the collected data is fed to the feature extraction stage, where the source code, process, and ownership metrics of all refactored and non-refactored data are retrieved for further processing. After that, the extracted features are predicted using Adaptive and Attentive Dilation Adopted Hybrid Network (AADHN) techniques, in which it is performed using Deep Temporal Context Networks (DTCN) with a Bidirectional Long-Short Term Memory (Bi-LSTM) model. Here, the parameters in the hybrid networking model are optimized with the help of Constant Integer Updated Golden Tortoise Beetle Optimizer (CIU-GTBO) for improving the prediction process. Therefore, the accuracy of the developed algorithm has achieved for different datasets, whereas it shows the value of 96.41, 96.38, 96.38, 96.38, 96.41, 96.38, and 96.39 for antlr4, junit, mapdb, mcMMO, mct, oryx, and titan. Also, the precision of the developed model has shown the better performance of 96.38, 96.32, 96.37, 96.33, 96.35, 96.37, and 96.31 for the datasets like antlr4, junit, mapdb, mcMMO, mct, oryx, and titan.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252622","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":"Analyzing the Correlation Between Toxic Comments and Code Quality","authors":"Jaime Sayago-Heredia,&nbsp;Gustavo Chango Sailema,&nbsp;Ricardo Pérez-Castillo,&nbsp;Mario Piattini","doi":"10.1002/smr.2739","DOIUrl":"https://doi.org/10.1002/smr.2739","url":null,"abstract":"<p>Software development has a relevant human side, and this could, for example, imply that developers' feelings have an impact on certain aspects of software development such as quality, productivity, or performance. This paper explores the effects of toxic emotions on code quality and presents the <i>SentiQ</i> tool, which gathers and analyzes sentiments from commit messages (obtained from GitHub) and code quality measures (obtained from SonarQube). The <i>SentiQ</i> tool we proposed performs a sentiment analysis (based on natural language processing techniques) and relates the results to the code quality measures. The datasets extracted are then used as the basis on which to conduct a preliminary case study, which demonstrates that there is a relationship between toxic comments and code quality that may affect the quality of the whole software project. This has resulted in the drafting of a predictive model to validate the correlation of the impact of toxic comments on code quality. The main implication of this work is that these results could, in the future, make it possible to estimate code quality as a function of developers' toxic comments.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252540","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":"From Backlogs to Bots: Generative AI's Impact on Agile Role Evolution","authors":"Philipp Diebold","doi":"10.1002/smr.2740","DOIUrl":"https://doi.org/10.1002/smr.2740","url":null,"abstract":"<p>This position paper investigates the transformative impact of generative artificial intelligence (GenAI) on Agile roles. Focusing on the product owner, developer, and scrum master, we analyze how GenAI redefines traditional tasks, encouraging a shift towards more strategic and creative functions. Through practical experience, we illustrate AI's role in enhancing Agile processes, its practices and emphasize the need for Agile practitioners to integrate AI understanding. These results highlight the balance between GenAI's efficiencies and Agile's human-centric principles, proposing research directions for AI-enriched Agile practices that promise to drive innovation and maintain the agility in a technologically progressive era.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112389","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":"A Systematic Literature Review for Investigating DevOps Metrics to Implement in Software Development Organizations","authors":"Ankur Kumar,&nbsp;Mohammad Nadeem,&nbsp;Mohammad Shameem","doi":"10.1002/smr.2733","DOIUrl":"https://doi.org/10.1002/smr.2733","url":null,"abstract":"<div>\u0000 \u0000 <p>DevOps is a collaborative software development process where practitioners work as a team to continuously develop, deploy, and deliver software. DevOps practices still need to be mature, and practitioners face numerous challenges while considering DevOps as a software development process. The mainstream research community has helped simplify the DevOps adoption process and eliminate complexities by developing DevOps maturity models. However, the current maturity frameworks cannot measure every component of DevOps and do not mention metrics as parameters for measuring different DevOps practices or features. Therefore, this study aims to identify metrics for measuring practices and activities responsible for DevOps implementation. The systematic literature review (SLR) method was used to determine the metrics needed to measure DevOps practices. Using SLR, we have identified 32 metrics from 57 articles. The metrics identified in this study can be used to measure the impact of the practices adopted for DevOps implementation within software development organizations. Furthermore, we divided the identified metrics into Dev and Ops categories and five significant categories based on the DevOps lifecycle. The classification of metrics in our study into diverse regions provides a conceptual framework and understanding of DevOps measures.</p>\u0000 </div>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120177","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":"Contributions to Systems Software and Service Process Improvement and Innovation Based on Recent Advances","authors":"Ricardo Colomo-Palacios,&nbsp;Richard Messnarz,&nbsp;Miklós Biró","doi":"10.1002/smr.2737","DOIUrl":"https://doi.org/10.1002/smr.2737","url":null,"abstract":"&lt;p&gt;This special issue comprises a selected set of high quality and extended articles of the 29th Systems, Software and Services Process Improvement (EuroSPI) Conference, held in 2022 in Salzburg, Austria.&lt;/p&gt;&lt;p&gt;Conferences were held in Dublin (Ireland) in 1994, in Vienna (Austria) in 1995, in Brighton (UK) in 1996, in Budapest (Hungary) in 1997, in Gothenburg (Sweden) in 1998, in Pori (Finland) in 1999, in Copenhagen (Denmark) in 2000, in Limerick (Ireland) in 2001, in Nuremberg (Germany) in 2002, in Graz (Austria) in 2003, in Trondheim (Norway) in 2004, in Budapest (Hungary) in 2005, in Joensuu (Finland) in 2006, in Potsdam (Germany) in 2007, in Dublin (Ireland) in 2008, in Alcala (Spain) in 2009, in Grenoble (France) in 2010, in Roskilde (Denmark) in 2011, in Vienna (Austria) in 2012, in Dundalk (Ireland) in 2013, in Luxembourg in 2014, in Ankara (Turkey) in 2015, in Graz (Austria) in 2016, in Ostrava (Czech Republic) in 2017, in Bilbao (Spain) in 2018, in Edinburgh (UK) in 2019, in Düsseldorf (Germany) in 2020, in Krems (Austria) in 2021, and in Salzburg (Austria) in 2022.&lt;/p&gt;&lt;p&gt;EuroSPI had a cooperation with the EU Blueprint for Automotive project DRIVES [&lt;span&gt;1&lt;/span&gt;] (2018–2022) where leading Automotive industry discusses and presents skills for the Europe 2030 strategy in the automotive sector. In 2022, the ASA (Automotive Skills Alliance) has been formed in Brussels which continues with the DRIVES results, and EuroSPI is a partner in the ASA.&lt;/p&gt;&lt;p&gt;EuroSPI has a cooperation with the EU Blueprint for Batteries project ALBATTS (2020–2023) where leading European industry discusses and establishes a skills agenda to build a European battery production capability for the electrification of European e-mobility.&lt;/p&gt;&lt;p&gt;EuroSPI has a cooperation with the EU project FLAMENCO [&lt;span&gt;2&lt;/span&gt;] (2023–2024) where for the Working Group 3.6 for IT in Automotive, the certification and training services for job roles in European automotive industry will be developed.&lt;/p&gt;&lt;p&gt;EuroSPI has established the SPI Manifesto (SPI = Systems, Software and Services Process Improvement [&lt;span&gt;3&lt;/span&gt;]), a set of social media groups including a selection of presentations and key notes freely available on YouTube and access to job role-based qualification through the European Certification and Qualification Association (www.ecqa.org).&lt;/p&gt;&lt;p&gt;A typical characterization of EuroSPI is reflected in a statement made by a company: “… the biggest value of EuroSPI lies in its function as a European knowledge and experience exchange mechanism for SPI and innovation.”&lt;/p&gt;&lt;p&gt;Since its beginning in 1994 in Dublin, the EuroSPI initiative continuously develops the term SPI including more and more fields that contribute. During the years, new communities (Cybersecurity, Internet of Things, Agile, etc …) joined and the term EuroSPI&lt;sup&gt;2&lt;/sup&gt; became European System, Software, Service, Safety, and Security Process, Product, Programming Improvement, Innovation, and Infrastructure. So in ","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120138","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":"Safe design and evolution of smart contracts using dynamic condition response graphs to model generic role-based behaviors","authors":"Yibin Xu,&nbsp;Tijs Slaats,&nbsp;Boris Düdder,&nbsp;Thomas Troels Hildebrandt,&nbsp;Tom Van Cutsem","doi":"10.1002/smr.2730","DOIUrl":"https://doi.org/10.1002/smr.2730","url":null,"abstract":"<p>Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed and evolved independently and cannot share a joint access control policy. This makes it challenging to ensure the correctness of access control properties and to maintain correctness when the contracts are adapted. We propose using dynamic condition response (DCR) graphs for role-based and declarative access control for smart contracts and techniques for test-driven modelling and refinement of DCR graphs to support the safe design and evolution of smart contracts. We show that they allow for capturing and visualizing a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports the straightforward declaration of access control rights, improved code auditing, test-driven modelling, and safe evolution of smart contracts and improves users' understanding.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119599","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":"Ranking guidance actions to support engineers in fulfilling process constraints","authors":"Anmol Bilal,&nbsp;Christoph Mayr-Dorn,&nbsp;Alexander Egyed","doi":"10.1002/smr.2729","DOIUrl":"https://doi.org/10.1002/smr.2729","url":null,"abstract":"<p>In safety-critical systems engineering, regulations such as Automotive SPICE, ISO26262, or ED-109A mandate software quality assurance measures to provide evidence that the developed system is high quality. The constraints that define quality assurance conditions during the engineering life cycle are often non-trivial. This paper addresses the challenges, engineers face who are unfamiliar with the precise constraints of various projects (e.g., when newly joining a company or switching between departments). Understanding how to fulfill a constraint is a time-consuming and challenging task as an engineer needs to determine the most suitable option (out of potentially many) to fulfill a constraint violation. To this end, we propose a guidance action ranking framework to provide engineers with the most relevant guidance actions. Our primary ranking algorithm analyzes in the background the actions that engineers have made in the past to resolve a constraint violation without requiring explicit feedback from them. We evaluated our framework on two real-world data sets: an open-source drone management and an industrial air traffic control software system. Concretely, we replay past engineering activities and measured whether, in the case of a constraint violation, our suggested guidance actions were indeed selected by the engineer. The evaluation results revealed that learning from prior guidance actions effectively identifies the most appropriate guidance actions (ranked top 1 or 2) when compared to ranking algorithms based on action simplicity and artifact property change frequency. Specifically, we achieve a median MRR of 0.95 for the first case study and 0.94 for the second case study: an improvement of 80% and 100% over the baseline. Additionally, we observed that the simplicity of a guidance action does not reliably indicate its suitability for fulfilling a constraint, whereas learning from prior change operation property out-performed simplicity-based ranking but did not surpass guidance frequency-based ranking.</p>","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2729","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117766","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":"Editorial for the special issue on software refactoring: Application breadth and technical depth","authors":"Zhenchang Xing","doi":"10.1002/smr.2732","DOIUrl":"https://doi.org/10.1002/smr.2732","url":null,"abstract":"&lt;p&gt;The field of software refactoring has been evolving rapidly, driven by the need to maintain software quality and manage complexity as software systems grow. This special issue of the &lt;i&gt;Journal of Software: Evolution and Process&lt;/i&gt; focuses on “Software Refactoring: Application Breadth and Technical Depth,” aiming to promote research that advances our understanding of software refactoring practices and their impact. The selected papers in this issue reflect the broad scope and technical depth of the topic, covering a variety of innovative methodologies, empirical studies, and new tools that push the boundaries of current software refactoring research.&lt;/p&gt;&lt;p&gt;The paper “Software Refactoring Side Effects” by AbuHassan, Alshayeb, and Ghouti addresses the often-overlooked side effects of software refactoring. The authors propose three different approaches for handling these side effects and employ a multi-objective optimization algorithm to identify optimal refactoring decisions. Their work enhances the understanding of refactoring's unintended consequences and proposes practical solutions, fitting well into the theme of exploring new dimensions of refactoring practices.&lt;/p&gt;&lt;p&gt;Zhao et al., in their article “An Architecture Refactoring Approach to Reducing Software Hierarchy Complexity,” introduce a novel metric to quantify software hierarchy complexity and propose the Complexity-oriented Software Architecture Refactoring (CoSSR) approach. Their study demonstrates how reducing software hierarchy complexity can improve software quality, aligning with the issue's focus on reducing complexity through innovative refactoring techniques.&lt;/p&gt;&lt;p&gt;The article “MARS: Detecting Brain Class/Method Code Smell Based on Metric-Attention Mechanism and Residual Network” by Zhang and Dong presents a new method for detecting complex code smells using a novel deep learning approach. By leveraging a metric-attention mechanism and an improved residual network, their model shows superior performance compared to existing methods. This research contributes to the special issue by applying cutting-edge machine learning techniques to refactoring challenges.&lt;/p&gt;&lt;p&gt;AlOmar et al.'s study, “Behind the Scenes: On the Relationship Between Developer Experience and Refactoring,” investigates the correlation between developer experience and refactoring activities across 800 open-source projects. Their findings highlight that experienced developers tend to perform more varied refactoring operations but document less of their refactoring activities. This study offers a new perspective on how human factors influence refactoring practices, enriching the broader conversation on effective software evolution.&lt;/p&gt;&lt;p&gt;In “A Study of Refactorings During Software Change Tasks,” Eilertsen and Murphy explore the reasons behind the disuse of refactoring tools during software change tasks. Their observational study reveals that the strategies developers use significantly impact the use of refactoring ","PeriodicalId":48898,"journal":{"name":"Journal of Software-Evolution and Process","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/smr.2732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116621","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}