Journal of Software: Evolution and Process最新文献

{"title":"Practitioners' expectations on automated release note generation techniques","authors":"Sristy Sumana Nath, Banani Roy","doi":"10.1002/smr.2657","DOIUrl":"https://doi.org/10.1002/smr.2657","url":null,"abstract":"The software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.","PeriodicalId":49024,"journal":{"name":"Journal of Software: Evolution and Process","volume":"53 41","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139798191","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":"GRRLN: Gated Recurrent Residual Learning Networks for code clone detection","authors":"Xiangping Zhang, Jianxun Liu, Min Shi","doi":"10.1002/smr.2649","DOIUrl":"https://doi.org/10.1002/smr.2649","url":null,"abstract":"Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.","PeriodicalId":49024,"journal":{"name":"Journal of Software: Evolution and Process","volume":"57 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139796925","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}