Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11253
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dc.contributor.authorUfuktepe, Ekincan-
dc.contributor.authorTuğlular, Tuğkan-
dc.date.accessioned2021-11-06T09:27:12Z-
dc.date.available2021-11-06T09:27:12Z-
dc.date.issued2021-
dc.identifier.isbn9781665424639-
dc.identifier.urihttp://doi.org/10.1109/COMPSAC51774.2021.00137-
dc.identifier.urihttps://hdl.handle.net/11147/11253-
dc.description45th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2021 -- 12 July 2021 through 16 July 2021en_US
dc.description.abstractCode changes are one of the essential processes of software evolution. These changes are performed to fix bugs, improve quality of software, and provide a better user experience. However, such changes made in code could lead to ripple effects that can cause unwanted behavior. To prevent such issues occurring after code changes, code change prediction, change impact analysis techniques are used. The proposed approach uses static call information, forward slicing, and method change information to build a Markov chain, which provides a prediction for code changes in the near future commits. For static call information, we utilized and compared call graph and effect graph. We performed an evaluation on five open-source projects from GitHub that varies between 5K-26K lines of code. To measure the effectiveness of our proposed approach, recall, precision, and f-measure metrics have been used on five open-source projects. The results show that the Markov chain that is based on call graph can have higher precision compared to effect graph. On the other hand, for small number of cases higher recall values are obtained with effect graph compared to call graph. With a Markov chain model based on call graph and effect graph, we can achieve recall values between 98%-100%. © 2021 IEEE.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofProceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChange impact analysisen_US
dc.subjectChange propagation predictionen_US
dc.subjectMarkov chainsen_US
dc.subjectSoftware evolutionen_US
dc.titleCode change sniffer: Predicting future code changes with Markov chainen_US
dc.typeConference Objecten_US
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.startpage1014en_US
dc.identifier.endpage1019en_US
dc.identifier.wosWOS:000706529000126en_US
dc.identifier.scopus2-s2.0-85115857218en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/COMPSAC51774.2021.00137-
dc.authorscopusid57063534000-
dc.authorscopusid14627984700-
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.dept03.04. Department of Computer Engineering-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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