Data quality issues in software fault prediction: a systematic literature review-Reference-Cited by-同舟云学术

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Data quality issues in software fault prediction: a systematic literature review

Published:2022-12-21 Issue: Volume: Page:
ISSN:0269-2821
Container-title:Artificial Intelligence Review
language:en
Short-container-title:Artif Intell Rev

Author:

Bhandari Kirti,Kumar Kuldeep,Sangal Amrit Lal

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10462-022-10371-6.pdf

Reference210 articles.

1. Abaei G, Selamat A (2014) Increasing the accuracy of software fault prediction using majority ranking fuzzy clustering. Int J Softw Innov 2:60–71. https://doi.org/10.4018/ijsi.2014100105

2. Adrion WR, Branstad MA, Cherniavsky JC (1982) Validation, verification, and testing of computer software. ACM Comput Surv 14:159–192. https://doi.org/10.1145/356876.356879

3. Agrawal A, Menzies T (2018) Is “better data” better than “better data miners”? In: Proceedings of the 40th International conference on software engineering. ACM, New York, pp 1050–1061

4. Alan O, Catal C (2009) An outlier detection algorithm based on object-oriented metrics thresholds. 2009 24th Int Symp Comput Inf Sci (ISC 2009), pp 567–570. https://doi.org/10.1109/ISCIS.2009.5291882

5. Alan O, Catal C (2011) Thresholds based outlier detection approach for mining class outliers: An empirical case study on software measurement datasets. Expert Syst Appl 38:3440–3445. https://doi.org/10.1016/j.eswa.2010.08.130

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