Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study

Khoshgoftaar, Taghi M.; Seliya, Naeem

doi:10.1023/b:emse.0000027781.18360.9b

Cited by 147 publications

(89 citation statements)

References 32 publications

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“…It was a case study of quality modeling for a very large telecommunications system. Two other publications of Khoshgoftaar and Seliya from 2004 [15] and 2005 [16] continued with the previous concept and focused on commercial data analysis, but were not applied to a real-world environment. A similar approach can be found in publications by Ostrand and Weyuker [29], Ostrand et al [31], Tosun et al [45], Turhan et al [47,48].…”

Section: Related Workmentioning

confidence: 99%

“…To follow our initial approach, we used the basic mechanisms built into DePress/KNIME by constructing a workflow as shown in 15 Cost Effectiveness of Software Defect ... Figure 2. First, the dataset is split into two parts, by classifying rows of two different sets, depending on the objective variable value ("1" or "0"), using the Row Splitter KNIME node.…”

Section: Objective Variable and Class Imbalance Counteractionmentioning

confidence: 99%

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Cost Effectiveness of Software Defect Prediction in an Industrial Project

Hryszko

Madeyski

2018

Foundations of Computing and Decision Sciences

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Abstract. Software defect prediction is a promising approach aiming to increase software quality and, as a result, development pace. Unfortunately, the cost effectiveness of software defect prediction in industrial settings is not eagerly shared by the pioneering companies. In particular, this is the first attempt to investigate the cost effectiveness of using the DePress open source software measurement framework (jointly developed by Wroclaw University of Science and Technology, and Capgemini software development company) for defect prediction in commercial software projects. We explore whether defect prediction can positively impact an industrial software development project by generating profits. To meet this goal, we conducted a defect prediction and simulated potential quality assurance costs based on the best possible prediction results when using a default, non-tweaked DePress configuration, as well as the proposed Quality Assurance (QA) strategy. Results of our investigation are optimistic: we estimated that quality assurance costs can be reduced by almost 30% when the proposed approach will be used, while estimated DePress usage Return on Investment (ROI) is fully 73 (7300%), and Benefits Cost Ratio (BCR) is 74. Such promising results, being the outcome of the presented research, have caused the acceptance of continued usage of the DePress-based software defect prediction for actual industrial projects run by Volvo Group.

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Section: Related Workmentioning

confidence: 99%

Section: Objective Variable and Class Imbalance Counteractionmentioning

confidence: 99%

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Hryszko

Madeyski

2018

Foundations of Computing and Decision Sciences

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“…Size metrics and complexity metrics are known as especially useful metrics for assessing the quality of the programs and for detecting poor-quality programs. This is because programs that are too large and/or too complex are often problematic, i.e., they are likely to have more potential faults or to be costly in their maintenance phases [6]- [11]. Such problematic programs should be carefully reviewed and tested as early as possible.…”

Section: Introductionmentioning

confidence: 99%

Lines of Comments as a Noteworthy Metric for Analyzing Fault-Proneness in Methods

Aman

Amasaki

Sasaki

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper focuses on the power of comments to predict fault-prone programs. In general, comments along with executable statements enhance the understandability of programs. However, comments may also be used to mask the lack of readability in the program, therefore well-written comments are referred to as "deodorant to mask code smells" in the field of code refactoring. This paper conducts an empirical analysis to examine whether Lines of Comments (LCM) written inside a method's body is a noteworthy metric for analyzing fault-proneness in Java methods. The empirical results show the following two findings: (1) morecommented methods (the methods having more comments than the amount estimated by size and complexity of the methods) are about 1.6 -2.8 times more likely to be faulty than the others, and (2) LCM can be a useful factor in fault-prone method prediction models along with the method size and the method complexity.

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“…The rest of the papers consists of 12 case studies [20][21][22][23][24][25][26][27][28][29][30][31], 11 reviews [32][33][34][35][36][37][38][39][40][41][42], 11 empirical analyzes [43][44][45][46][47][48][49][50][51][52][53], three comparative analyzes ( [54][55][56]) and two field studies [57,58].…”

Section: Introductionmentioning

confidence: 99%

Rethinking Experiments in a Socio-Technical Perspective: The Case of Software Engineering

Schiaffonati

Verdicchio

2015

Philosophies

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Experiments in computing share many characteristics with the traditional experimental method, but also present significant differences from a practical perspective, due to their aim at producing software artifacts and the central role played by human actors and organizations (e.g., programmers, project teams, software houses) involved in the software development process. By analyzing some of the most significant experiments in the subfield of software engineering, we aim at showing how the conceptual framework that supports experimental methodology in this context needs an extension in a socio-technical perspective.

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Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study

Cited by 147 publications

References 32 publications

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Lines of Comments as a Noteworthy Metric for Analyzing Fault-Proneness in Methods

Rethinking Experiments in a Socio-Technical Perspective: The Case of Software Engineering

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