• Anurag Sinha
• Shubham singh
• Devansh Kashyap

Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspect becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product. Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may analyze software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques. Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects. Software quality metrics are a subset of software metrics that f software defect prediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality. In this paper, various classification are revisited which are employed for software defect prediction using software metrics in the literature.

Software Metrics, Software Quality software engineering tasks, data mining, text mining, classification, clustering Software Defect Prediction, Software Metrics, Classification.

"Implication of Data Mining and Machine Learning in Software Engineering Domain for Software Model, Quality and Defect Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.1453-1467, October-2020, Available :http://www.jetir.org/papers/JETIR2010186.pdf

"Implication of Data Mining and Machine Learning in Software Engineering Domain for Software Model, Quality and Defect Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp1453-1467, October-2020, Available at : http://www.jetir.org/papers/JETIR2010186.pdf