• Mr. Vatti Srikanth
• Mr. Ch. Srinivasa Reddy

Even with meticulous planning, thorough documentation, and suitable procedures, some faults in software are unavoidable. These software flaws could result in a drop in quality, which could be the root cause of failure. Different techniques have been developed by researchers for efficient software defect prediction. A software module's vulnerability to flaws or defects can be predicted, which can speed up testing by allowing developers and testers to focus their efforts and resources on those modules. In this project, we provide Code Vectorizer, a revolutionary technique for defect prediction that extracts information from the language of the software source code itself. Despite of great planning, well documentation and proper process during software development, occurrences of certain defects are inevitable. These software defects may lead to degradation of the quality which might be the underlying cause of failure. Researchers have devised various methods that can be used for effective software defect prediction. The prediction of the presence of defects or bugs in a software module can facilitate the testing process as it would enable developers and testers to allocate their time and resources on modules that are prone to defects.

Code Vectorizer,Prediction,Software Modules,Vulnerability,Software Development.

"Code Vectorizer Based Machine Learning Models for Software Defect Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e299-e302, February-2024, Available :http://www.jetir.org/papers/JETIR2402442.pdf

"Code Vectorizer Based Machine Learning Models for Software Defect Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe299-e302, February-2024, Available at : http://www.jetir.org/papers/JETIR2402442.pdf