Journal ArticleDOI
Software fault prediction metrics
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TLDR
Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics and seem to be better at predicting post-release faults than any static code metrics.Abstract:
ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.read more
Citations
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Journal ArticleDOI
A systematic review of machine learning techniques for software fault prediction
TL;DR: The machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers, however, the application of theMachine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results.
Journal ArticleDOI
An empirical study on software defect prediction with a simplified metric set
TL;DR: The experimental results indicate that the choice of training data for defect prediction should depend on the specific requirement of accuracy and the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.
Journal ArticleDOI
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
TL;DR: CPDP is still a challenge and requires more research before trustworthy applications can take place and this work synthesises literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances.
Journal ArticleDOI
A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction
TL;DR: Imbalanced learning should only be considered for moderate or highly imbalanced SDP data sets and the appropriate combination of imbalanced method and classifier needs to be carefully chosen to ameliorate the imbalanced learning problem for SDP.
References
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Proceedings ArticleDOI
Predicting defects in SAP Java code: An experience report
TL;DR: The overall predictive power of these regression models is lower than expected; still, the resulting regression models successfully predicted 50–60% of the 20% most defect-prone components.
Proceedings ArticleDOI
Prediction of fault-proneness at early phase in object-oriented development
TL;DR: A new method to estimate the fault-proneness of an object class in the early phase, using several complexity metrics for object-oriented software.
Journal ArticleDOI
Improving the applicability of object-oriented class cohesion metrics
TL;DR: Having the class cohesion metrics defined for all possible cases improves the applicability of the metrics and potentially increases their precision in indicating class quality.
Proceedings ArticleDOI
Defect Prediction using Combined Product and Project Metrics - A Case Study from the Open Source "Apache" MyFaces Project Family
TL;DR: This paper investigates defect prediction with data from a family of widely used OSS projects based both on product and project metrics as well as on combinations of these metrics to improve the accuracy of defect prediction and enable a better guidance of the release process from project management point of view.
Book ChapterDOI
Empirical Analysis of the Relation between Level of Detail in UML Models and Defect Density
TL;DR: A novel and practical approach to measure LoD in UML models is discussed and its application to a significant industrial case study is described.