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The Role of Process in Early Software Defect Prediction: Methods, Attributes and Metrics

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Software Process Improvement and Capability Determination (SPICE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 609))

Abstract

Software quality is the set of inherent characteristics that are built into a software product throughout software development process. An important indicator of software quality is the trend of software defects in the life-cycle. The models of software defect prediction and software reliability provide the opportunity for practitioners to observe the defectiveness distribution of their products in development and operation. However, reported studies are mostly focused on coding or testing stages. Though this is reasonable due to executable nature of the product, it prevents practitioners from taking the advantages (such as cost reduction) of identifying and predicting software defects earlier in the life-cycle. This paper, therefore, provides an overview of the trend of early software defect prediction studies as retrieved by a systematic mapping of the literature, and elaborates on the methods, attributes, and metrics of the studies that comprise software process data in the defect prediction.

The original version of this chapter was revised. An erratum to this chapter can be found at 10.1007/978-3-319-38980-6_34

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-38980-6_34

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Correspondence to Rana Ozakinci .

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A Appendix

A Appendix

See Table 6.

Table 6. Software defect prediction studies that covered process-based data

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Ozakinci, R., Tarhan, A. (2016). The Role of Process in Early Software Defect Prediction: Methods, Attributes and Metrics. In: Clarke, P., O'Connor, R., Rout, T., Dorling, A. (eds) Software Process Improvement and Capability Determination. SPICE 2016. Communications in Computer and Information Science, vol 609. Springer, Cham. https://doi.org/10.1007/978-3-319-38980-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-38980-6_21

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