Quantitative Analysis of Faults and Failures in a Complex Software System

By N. Fenton, Niclas Ohlsson. IEEE Trans. Software Eng., 2000

759 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/11d038add375cb0c06baf9797ad13b8201b80cb4

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Citations in the corpus

Software metrics: roadmap (2000, neutral): “A further major empirical study [22] sheds considerable light on why the above approach is fundamentally insufficient.”
Increasing diversity: Natural language measures for software fault prediction (2009, neutral): “(Bell et al., 2006; Fenton and Ohlsson, 2000; Gyimóthy et al., 2005; Koru and Tian, 2007; Menzies et al., 2007; Munson and Khoshgoftaar, 1992).”
Software fault prediction metrics: A systematic literature review (2013, neutral): “prediction, and if so which ones? Complexity metrics do have some predictive capabilities, but may not be the best metrics for software fault prediction, since they are highly correlated with lines of code [84,88,112,154].”
Predicting defects using network analysis on dependency graphs (2008, neutral): “Although this is not always true, this assumption is frequently used in research [15, 30, 35, 41].”
Data Mining Static Code Attributes to Learn Defect Predictors (2007, neutral): “Our conclusion is that, contrary to prior pessimism [21],”
An empirical study of operating systems errors (2001, neutral): “Fenton and Ohlsson [9] examined faults in two consecutive versions of a telecommunication switching system.”
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (2009, neutral): “There is an extensive body of knowledge on the metrics discussed above when used for predicting quality [12, 13, 19, 22, 28, 29].”
Predicting the location and number of faults in large software systems (2005, neutral): “This fault counting convention is consistent with the ones used in [10] and [3], and in our earlier studies [13], [14].”
Have things changed now?: an empirical study of bug characteristics in modern open source software (2006, neutral): “Many efforts have been made to study fault related characteristics of large software systems [8, 10, 16 , 19, 28, 29, 30, 32, 36].”
The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics (2001, neutral): “This is motivated by evidence showing that most faults are found in only a few of a system’s components [86], [ 51 ], [67], [91].”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, positive): “From our experiences in software defect prediction [37] and from other people’s work on quantitative analysis of fault distributions [14, 29], we know that statistically, larger files are more likely to contain bugs.”
Coverage is not strongly correlated with test suite effectiveness (2014, positive): “Third, in object-oriented systems, most faults are usually found in just a few of the system’s components [12].”
Predicting defect-prone software modules using support vector machines (2008, positive): “Studies have shown that the majority of defects are often found in only a few software modules (Fenton and Ohlsson, 2000; Koru and Tian, 2003).”
The prediction of faulty classes using object-oriented design metrics (2001, positive): “Recent evidence indicates that most faults in software applications are found in only a few of a system’s components (Fenton and Ohlsson, 2000; Kaaniche and Kanoun, 1996; Moller and Paulish, 1993; Ohlsson and Alberg, 1996).”
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008, critical): “However, for other software projects this seems not to work, as the post-release defect distribution might be completely different from the pre-release defect distribution [19].”
An extensive comparison of bug prediction approaches (2010, neutral): “The jury is still out on the relative performance of these approaches.”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, neutral): “(2005), and of Fenton and Ohlsson (2000). This raises the question of whether the results we outlined above are valid with other machine learning approaches.”