Software defect estimation is a critical aspect of software development that helps manage project schedules, allocating resources effectively, and ensuring software quality. Various techniques have been proposed and used to estimate software defects, including historical data analysis, expert judgment, function point analysis, software metrics, and machine learning approaches. This comprehensive research survey paper aims to review and compare these techniques in terms of their strengths, weaknesses, and applicability in different software development contexts. The findings from this analysis will assist researchers, practitioners, and decision-makers in selecting appropriate defect estimation techniques for their specific software projects. Through an extensive literature review and analysis of case studies, this paper evaluates the strengths and weaknesses of each technique, highlighting their accuracy, precision, data requirements, scalability, transparency, interpretability, flexibility, and resource requirements. Furthermore, the applicability of these techniques in various software development contexts, such as small-scale projects, medium-scale projects, large-scale projects, agile development environments, safety-critical systems, open-source projects, and distributed development teams, is examined. It also identifies areas for future research and improvement, paving the way for the development of more accurate and effective defect estimation methods.
Software defect estimation, machine learning, artificial intelligence.
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