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While version control highlights differences between versions, it may not adequately alert developers to potential side effects of changes. Change impact analysis alongside code differences can guide reviewers in making informed decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aim to enhance the code review process by providing helpful insights to reviewers by analyzing change impact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We propose a novel change impact analysis approach with the granularity of pull requests by combining call graph-based dependency analysis and history mining techniques. By utilizing these, we calculate several file metrics and an overall risk score for each pull request. To validate the approach, we conducted two focus group sessions, including a feature feedback survey, a tool demo, a post-demo survey, and focus group discussions. Additionally, we performed experiments on three open-source projects to evaluate the computational feasibility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From the focus groups, our approach received an average of 3.66 out of 5.0 for enhancing the code review experience across five aspects. The current risk score formula averaged 3.2 out of 5.0 for accurately representing change impact, with 71% agreement on the selected metrics. Experiments confirmed computational viability, with analysis times ranging from 7.4 to 22.43 seconds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study underscores the potential benefits of integrating change impact analysis into the code review process. By leveraging this approach, developers can conduct more thorough reviews, enhancing their ability to detect and mitigate potential issues arising from code changes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10664-024-10600-2","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T01:14:46Z","timestamp":1739322886000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Enhanced code reviews using pull request based change impact analysis"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7359-6937","authenticated-orcid":false,"given":"Ismail Sergen","family":"G\u00f6\u00e7men","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7021-260X","authenticated-orcid":false,"given":"Ahmed Salih","family":"Cezayir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5550-7816","authenticated-orcid":false,"given":"Eray","family":"T\u00fcz\u00fcn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"10600_CR1","doi-asserted-by":"publisher","unstructured":"Acharya M, Robinson B (2011) Practical change impact analysis based on static program slicing for industrial software systems. 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