Predicting the location and number of faults in large software systems

By Thomas J. Ostrand, Elaine J. Weyuker, Robert M. Bell. IEEE Transactions on Software Engineering, 2005

753 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/21305504e7ccde5505a4281419a5799bccc42378

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Software

Citations in the corpus

Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, background): “, lines of code and complexity), historical metrics [20, 46] (e.”
Classifying Software Changes: Clean or Buggy? (2008, background): “The goal of some work is to identify a problematic module list by analyzing software quality metrics or a project’s change history [13], [15], [16], [21], [39], [40], [41].”
Predicting Defects for Eclipse (2007, methodology): “[14] used historical data from up to 17 releases to predict the files with the highest defect density in the next release.”
A large-scale empirical study of just-in-time quality assurance (2013, background): “Prior work [50] showed that the majority of the defects (approximately 80 percent) are contained in a small number of files (approximately 20 percent).”
Software fault prediction metrics: A systematic literature review (2013, methodology): “The performance difference be-tween modeling techniques appears to be moderate [38,30,60] and the choice of a modeling technique seems to have lesser impact on classiﬁcation accuracy of a model than the choice of a metrics set [60].”
On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017, background): “This is also in agreement with previous studies that used the past fault-proneness history of classes to predict their future faults (Ostrand et al. 2005).”
An extensive comparison of bug prediction approaches (2010, methodology): “predict faults on two industrial systems, using change and defect data [14].”
Transfer defect learning (2013, methodology): “To evaluate our proposed approaches, we conduct experiments in different settings: within-project defect prediction, cross-project defect prediction without transfer learning, and cross-project defect prediction with TCA or TCA+. 1) Within-project Prediction: We present the within-project defect…”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, background): “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.”
Dealing with noise in defect prediction (2011, methodology): “Defect prediction is a very active area in software engineering research [7, 10, 11, 13, 18, 19, 33, 35].”
Mining metrics to predict component failures (2006, methodology): “In recent years, researchers have learned to exploit the vast amount of data that is contained in software repositories such as version and bug databases [16, 17, 19, 22].”
Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “Nagappan et al. [32] apply principle component analysis (PCA), while Bell et al. [3] and Ostrand et al. [35] use negative binomial regression to build models that predict fault-prone (cid:12)les.”
Software Testing Research: Achievements, Challenges, Dreams (2007, background): “Banally, for all three aspects, the barrier is cost: careful empirical studies on large scale products, within real world contexts (such as [66]), and possibly replicated by several professional testers so to attain generally valid results are of course prohibitively expensive.”
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults (2006, background): “…of software development managers seeking to produce high-quality software on time and within budget [25], and indeed the last three decades has seen proposed many software fault prediction models [1], [2], [4], [7], [8], [9], [10], [11], [12], [13], [15], [16], [17], [23], [24], [25], [26], [27].”
Towards identifying software project clusters with regard to defect prediction (2010, methodology): “However, it must be mentioned that Weyuker et al. used another approach as the one that is presented in this paper.”