Simulation of Software Engineering Knowledge Classification Model Based on Worker Bee Colony Algorithm

The strict review and test emphasized by traditional software engineering cannot guarantee the correct operation of the system, just as the best programmer cannot guarantee that the program is defect-free, the best system built with the latest technology cannot guarantee that it can handle all problems in the actual operation process. There are many ways to improve the security of software projects, such as cultivating developers' good programming skills, adopting a sound development model, and so on. In practical practice, it has also achieved quite good results. However, according to its theory, in the case of large amount of data, the traditional algorithm needs to scan and calculate the loss of all features to determine the optimal segmentation point, which is extremely time-consuming in practice. Before the classification model analyzes the software change log, a large number of change logs need to be preprocessed, including sentence segmentation, word segmentation, removal of stop words, extraction of part of speech, extraction of stem, and other steps. The effect of pretreatment will directly affect the development of subsequent experiments. Making full use of these rules and static testing tools of ABC(Artificial bee colony algorithm) software engineering knowledge classification model and conducting static testing in time can greatly improve the correctness, security and reliability of software products.