• Meer Tauseef Ali
• Dr .Syed Abdul Sattar

In order to estimate the sensitivity of application modules to errors in software engineering, many computational approaches have been proposed. Classifying a program module as vulnerable to error means the execution of multiple verification operations. The wrong designation of a module as free of faults includes the possibility of device failure and has repercussions for costs as well. The selection of the "best" candidate from the several available models involves performance assessment and thorough comparisons, but due to the applicability of various performance indicators, these comparisons are not simple. This research identifies the benefits and disadvantages of performance assessment approaches and suggests that without taking into consideration the expense characteristics of the project unique to each implementation environment, the option of a "optimal" model will not be made. At the root of scientific software engineering research should be accurate methods for analyzing fault prediction models, but they have gained only scattered attention so far. In comparison to the numerous approaches currently used in software engineering research, this paper offers a description of model estimation methods by introducing and explaining the advantages of cost factors.

Using different ML methods for assessment of software defect prediction model

"Using different ML methods for assessment of software defect prediction model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.389-397, August-2023, Available :http://www.jetir.org/papers/JETIR1908E55.pdf

"Using different ML methods for assessment of software defect prediction model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. pp389-397, August-2023, Available at : http://www.jetir.org/papers/JETIR1908E55.pdf