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Data Mining Techniques for Software Quality Prediction

Data Mining Techniques for Software Quality Prediction

Bharavi Mishra, K. K. Shukla
Copyright: © 2014 |Pages: 28
ISBN13: 9781466643017|ISBN10: 1466643013|EISBN13: 9781466643024
DOI: 10.4018/978-1-4666-4301-7.ch021
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MLA

Mishra, Bharavi, and K. K. Shukla. "Data Mining Techniques for Software Quality Prediction." Software Design and Development: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 401-428. https://doi.org/10.4018/978-1-4666-4301-7.ch021

APA

Mishra, B. & Shukla, K. K. (2014). Data Mining Techniques for Software Quality Prediction. In I. Management Association (Ed.), Software Design and Development: Concepts, Methodologies, Tools, and Applications (pp. 401-428). IGI Global. https://doi.org/10.4018/978-1-4666-4301-7.ch021

Chicago

Mishra, Bharavi, and K. K. Shukla. "Data Mining Techniques for Software Quality Prediction." In Software Design and Development: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 401-428. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4301-7.ch021

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Abstract

In the present time, software plays a vital role in business, governance, and society in general, so a continuous improvement of software productivity and quality such as reliability, robustness, etc. is an important goal of software engineering. During software development, a large amount of data is produced, such as software attribute repositories and program execution trace, which may help in future development and project management activities. Effective software development needs quantification, measurement, and modelling of previous software artefacts. The development of large and complex software systems is a formidable challenge which requires some additional activities to support software development and project management processes. In this scenario, data mining can provide a helpful hand in the software development process. This chapter discusses the application of data mining in software engineering and includes static and dynamic defect detection, clone detection, maintenance, etc. It provides a way to understand the software artifacts and processes to assist in software engineering tasks.

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