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These days, software systems are very complex and versatile. Therefore it is essential to identify and fix the software error. Software error assessment is one of the most active areas of research in software engineering. In this research, we are introducing soft computing methods to assess software errors. Our proposed technique ts software gives errors and accurate results. In our proposed method, the error database is first extracted, which acts as an input. After that, the collected input (data) is clustered by the clustering technique. For this purpose, we use the modified C-Mean Algorithm. Therefore, the data is clustered. An efficient classification algorithm then groups clustered data. For this reason, we use a hybrid nervous system. Therefore, there are software bugs, and these errors are optimized using the MCS algorithm. Our proposed method for software error assessment is implemented on the Java platform. Performance measurement is measured by various parameters such as execution rate and execution time. Our proposed Cuckoo search based strategy is comparable to many existing strategies. Graphical representation of comparison results from our proposed strategy for identifying software proposals is one that effectively evaluates profitable strategy and reasonable reference rates. |
