A systematic review of software fault prediction studies

By Cagatay Catal, Banu Diri. Expert systems with applications, 2009

572 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/c4b2def0b73349aa04ef4ae3f620c83129558e9f

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In Computer Networks and Communications

In Information Systems

In Software

Citations in the corpus

Using mapping studies as the basis for further research - A participant-observer case study (2011, background): “only report aggregations such as the number of primary studies in each category), for example [27].”
Fair and balanced?: bias in bug-fix datasets (2009, unknown): “A recent survey by Catal & Diri [11] lists almost 100 citations.”
Researcher Bias: The Use of Machine Learning in Software Defect Prediction (2014, methodology): “F”
A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, unknown): “In addition, we report on the performance of models and synthesise the ﬁndings of studies.”
Data Quality: Some Comments on the NASA Software Defect Datasets (2013, background): “A 2009 Mapping Study [4] identified 74 relevant studies, and this had grown to 208 by the end of 2010 (as reported by Hall et al. [7]).”
Software fault prediction metrics: A systematic literature review (2013, methodology): “(The modeling techniques are covered in more detail in [23,10,11].”
Predicting Vulnerable Software Components via Text Mining (2014, background): “This is an important achievement, as fixing a bug after the software has been released can cost much more than resolving the issue at development time [2].”
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques (2017, background): “Extensive research is performed and published on the subject of fault prediction models in the domain of software engineering that are reviewed in survey articles (Catal and Diri 2009; Malhotra 2015).”
A systematic review of machine learning techniques for software fault prediction (2015, background): “The existing literature reviews aim to answer various research questions [3-5], but to the best of the authors knowledge there is no systematic review that focus on the ML techniques for the software fault prediction i.”
An investigation on the feasibility of cross-project defect prediction (2011, background): “…defect predictors (Menzies et al. 2007b; Ohlsson and Alberg 1996; Nagappan and Ball 2005; Nagappan et al. 2006; Ostrand et al. 2005; Weyuker et al. 2008; Catal and Diri 2009; Hassan and Holt 2005; Wahyudin et al. 2008; Carvalho et al. 2010; D’Ambros et al. 2010; Jureczko and Spinellis 2010).”