Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Olague, Hector M.; Etzkorn, Letha H.; Gholston, Sampson; Quattlebaum, S.

doi:10.1109/tse.2007.1015

Cited by 259 publications

(170 citation statements)

References 17 publications

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“…Using the results of static source code analyzers is one of the most widespread solutions to calculate an external quality attribute from internal quality attributes [5]. There are several case studies about that the metrics are appropriate indicators for external quality attributes such as code fault proneness [7] [9] [15], maintainability [1] and attractiveness [14].…”

Section: Related Workmentioning

confidence: 99%

Towards Building Method Level Maintainability Models Based on Expert Evaluations

Hegedűs

Ladányi

Siket

et al. 2012

Computer Applications for Software Engineering, Disaster Recovery, and Business Continuity

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Abstract. The maintainability of software systems is getting more and more attention both from researchers and industrial experts. This is due to its direct impact on development costs and reliability of the software. Many models exist for estimating maintainability by aggregating low level source code metrics. However, very few of them are able to predict the maintainability on method level; even fewer take subjective human opinions into consideration. In this paper we present a new approach to create method level maintainability prediction models based on human surveys using regression techniques. We performed three di erent surveys and compared the derived prediction models. Our regression models were built based on approximately 150 000 answers of 268 persons. These models were able to estimate the maintainability of methods with a 0.72 correlation and a 0.83 mean absolute error on a continuous [0,10].

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Section: Related Workmentioning

confidence: 99%

Towards Building Method Level Maintainability Models Based on Expert Evaluations

Hegedűs

Ladányi

Siket

et al. 2012

Computer Applications for Software Engineering, Disaster Recovery, and Business Continuity

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“…Explicit variables specification of file defect proneness Pai et al [22] Implicit goal description Explicit research question with no hypotheses Explicit variables specification for number of defect per class and class defect proneness Olague et al [19] Ecxplicit goal statement Explicit research hypotheses to describe proposed goal Implicit variable specification to predict class defect proneness…”

Section: Explicit Research Hypothesesmentioning

confidence: 99%

“…Depicts the need for model calibration or refinement Nagappan et al [16] State briefly with no data N/A Li et al [11] Cross validation with different releases N/A Weyuker [27] N/A N/A Menzies [13] N/A N/A Graves [3] N/A N/A Sunghun [25] N/A N/A Pai et al [22] N/A N/A Olague et al [19] N/A N/A Table 2 outlines the collected data regarding common practices to construct prediction models. Most of these studies used variable selection prior to model construction.…”

Section: Explicit Research Hypothesesmentioning

confidence: 99%

A Framework for Defect Prediction in Specific Software Project Contexts

Wahyudin

Ramler

Biffl

2011

Software Engineering Techniques

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Abstract. Software defect prediction has drawn the attention of many researchers in empirical software engineering and software maintenance due to its importance in providing quality estimates and to identify the needs for improvement from project management perspective. However, most defect prediction studies seem valid primarily in a particular context and little concern is given on how to find out which prediction model is well suited for a given project context. In this paper we present a framework for conducting software defect prediction as aid for the project manager in the context of a particular project or organization. The framework has been aligned with practitioners' requirements and is supported by our findings from a systematical literature review on software defect prediction. We provide a guide to the body of existing studies on defect prediction by mapping the results of the systematic literature review to the framework.

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“…If not, changes to the cou pled objects in the future could result in relationships being modified that are unexpected. CBO has been shown to be correlated to class quality (defect or error-proneness of a class) [4,21,39,40,34,20]. Creating mock objects to test within the coupled objects also provide additional complexities between objects further increasing the infinitely difficult problem of testing a system.…”

Section: Applicabilitymentioning

confidence: 99%

“…There have been several proposals [6,7,12,17,28,27,19,9] and validations [4,21,39,40,34,20] in order to provide accurate predictions of class quality. As of now there is no oracle, however the seminal metrics have been tried and true compared to other more recent additions.…”

Section: Chaptermentioning

confidence: 99%

A Coupling-Complexity Metric Suite for Predicting Software Quality

Gray¹

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Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes

Cited by 259 publications

References 17 publications

Towards Building Method Level Maintainability Models Based on Expert Evaluations

Towards Building Method Level Maintainability Models Based on Expert Evaluations

A Framework for Defect Prediction in Specific Software Project Contexts

A Coupling-Complexity Metric Suite for Predicting Software Quality

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