Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

Hryszko, Jaroslaw; Madeyski, Lech

doi:10.1515/fcds-2015-0002

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…During our previous research, we recognized elements occurring in projects that hindered or prevented completion of prediction [8]. The most important of these elements are:…”

Section: Target Software Project and Processmentioning

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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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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“…During our previous research, we recognized elements occurring in projects that hindered or prevented completion of prediction [8]. The most important of these elements are:…”

Section: Target Software Project and Processmentioning

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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“…Project lacks of bottlenecks described by Hryszko and Madeyski [8], which could hinder or prevent from applying defect prediction process. However, we observed relatively high number of naming issues in the project.…”

Section: Research Environment: Industrial Software Development Projectmentioning

confidence: 99%

Defect prediction with bad smells in code

Hryszko,

Madeyski,

Dąbrowska

et al. 2017

Preprint

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Streszczenie Background. Defect prediction in software can be highly beneficial for development projects, when prediction is highly effective and defect-prone areas are predicted correctly. One of the key elements to gain effective software defect prediction is proper selection of metrics used for dataset preparation.Objective. The purpose of this research is to verify, whether code smells metrics, collected using Microsoft CodeAnalysis tool, added to basic metric set, can improve defect prediction in industrial software development project.Results. We verified, if dataset extension by the code smells sourced metrics, change the effectiveness of the defect prediction by comparing prediction results for datasets with and without code smells-oriented metrics. In a result, we observed only small improvement of effectiveness of defect prediction when dataset extended with bad smells metrics was used: average accuracy value increased by 0.0091 and stayed within the margin of error. However, when only use of code smells based metrics were used for prediction (without basic set of metrics), such process resulted with surprisingly high accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data anomalies and problems we observed when two different metric sources were used to prepare one, consistent set of data. Conclusion. Extending the dataset by the code smells sourced metric does not significantly improve the prediction effectiveness. Achieved result did not compensate effort needed to collect additional metrics. However, we observed that defect prediction based on the code smells only is still highly effective and can be used especially where other metrics hardly be used.

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Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project

Hryszko

Madeyski

2016

Advances in Intelligent Systems and Computing

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Bottlenecks in Software Defect Prediction Implementation in Industrial Projects

Cited by 8 publications

References 22 publications

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Cost Effectiveness of Software Defect Prediction in an Industrial Project

Defect prediction with bad smells in code

Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project

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