Abstract
In order to improve the efficiency and quality of software testing, aiming at various factors affecting software reliability, how to find defective modules and optimize them in the early stage of software development has become an urgent problem to be solved, This paper introduces the software defect prediction technology based on life cycle. According to the measurement elements affecting software reliability, relevant internal indicators and design defects, find the defect module, lock it in advance, adopt machine learning technology and reasonably allocate limited resources, which is conducive to evaluate the software design scheme, optimize the design strategy, reduce design changes and improve the software operation process, It plays a role in cost evaluation, resource management, scheme determination and quality prediction in software management. It is hoped to provide some theoretical support and practical reference for the development of software defect prediction.
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Peng, X., Ma, Z., Zhang, N., Huang, Y., Qi, M. (2023). Lifecycle-Based Software Defect Prediction Technology. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. AIC 2022. Lecture Notes in Electrical Engineering, vol 871. Springer, Singapore. https://doi.org/10.1007/978-981-99-1256-8_4
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DOI: https://doi.org/10.1007/978-981-99-1256-8_4
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