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The SZZ algorithm has two phases to detect defect-inducing commits: (1) linking issue reports in an issue-tracking system to possible <jats:italic>defect-fixing commits<\/jats:italic> in a version control system by using an <jats:italic>issue-link algorithm (ILA)<\/jats:italic>; and (2) tracing the modifications of defect-fixing commits back to possible defect-inducing commits. Researchers and practitioners can address the second phase by using existing solutions such as a tool called . In contrast, although various ILAs have been proposed for the first phase, no large-scale studies exist in which such ILAs are evaluated under the same experimental conditions. Hence, we still have no conclusions regarding the best-performing ILA for the first phase. In this paper, we compare 10 ILAs collected from our systematic literature study with regards to the accuracy of detecting defect-fixing commits. In addition, we compare the defect prediction performance of ILAs and their combinations that can detect defect-fixing commits accurately. We conducted experiments on five open-source software projects. We found that all ILAs and their combinations prevented the defect prediction model from being affected by missing defect-fixing commits. In particular, the combination of a natural language text similarity approach, Phantom heuristics, a random forest approach, and a support vector machine approach is the best way to statistically significantly reduced the absolute differences from the <jats:italic>ground-truth defect prediction performance<\/jats:italic>. We summarized the guidelines to use ILAs as our recommendations.<\/jats:p>","DOI":"10.1007\/s10664-022-10120-x","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T08:06:02Z","timestamp":1658477162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An empirical study of issue-link algorithms: which issue-link algorithms should we use?"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6317-7001","authenticated-orcid":false,"given":"Masanari","family":"Kondo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutaro","family":"Kashiwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasutaka","family":"Kamei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osamu","family":"Mizuno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"10120_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal A, Menzies T (2018) Is \u201cbetter data\u201d better than \u201cbetter data miners\u201d?. 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