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Effort-aware metrics (EAMs) relate to benefits provided by a classifier in accurately ranking defective entities such as classes or methods. PofB is an EAM that relates to a user that follows a ranking of the probability that an entity is defective, provided by the classifier. Despite the importance of EAMs, there is no study investigating EAMs trends and validity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Aim<\/jats:title><jats:p>The aim of this paper is twofold: 1) we reveal issues in EAMs usage, and 2) we propose and evaluate a normalization of PofBs (aka NPofBs), which is based on ranking defective entities by predicted defect density.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>We perform a systematic mapping study featuring 152 primary studies in major journals and an empirical study featuring 10 EAMs, 10 classifiers, two industrial, and 12 open-source projects.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our systematic mapping study reveals that most studies using EAMs use only a single EAM (e.g., PofB20) and that some studies mismatched EAMs names. The main result of our empirical study is that NPofBs are statistically and by orders of magnitude higher than PofBs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In conclusion, the proposed normalization of PofBs: (i) increases the realism of results as it relates to a better use of classifiers, and (ii) promotes the practical adoption of prediction models in industry as it shows higher benefits. Finally, we provide a tool to compute EAMs to support researchers in avoiding past issues in using EAMs.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s10664-022-10186-7","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T07:14:24Z","timestamp":1659770064000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["On effort-aware metrics for defect prediction"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9315-3652","authenticated-orcid":false,"given":"Jonida","family":"\u00c7arka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8451-3668","authenticated-orcid":false,"given":"Matteo","family":"Esposito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6340-0058","authenticated-orcid":false,"given":"Davide","family":"Falessi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"10186_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal A, Menzies T (2018) Is \u201cbetter data\u201d better than \u201cbetter data miners\u201d?: On the benefits of tuning SMOTE for defect prediction. 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