Treffer: Software defect prediction using machine learning techniques.

Software defects are defined as a range of circumstances that affect how well a software system operates and performs. Early detection of the defects is crucial for software development. Software defect prediction, or SDP, is a rapid and economical way to increase the software quality by predicting faults early in the development phase. In order to improve software quality and dependability, software defect prediction utilizing machine learning approaches have become essential component of software engineering. Machine learning allows us to evaluate large volumes of historical data to find trends and patterns related to software flaws in defect prediction. A thorough evaluation of the literature is used to identify trends, gaps, and opportunities for improvement in the machine-learning approaches and techniques currently used in SDP. The paper's objectives are to study the literature that has already been written and analyze the effectiveness of machine learning systems for software defect prediction. The review paper gives an insight into the significance, difficulties, approaches, and goals related to software defect prediction using machine learning techniques. [ABSTRACT FROM AUTHOR]

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