Analysis of Effectiveness of Apriori and Frequent Pattern Tree Algorithm in Software Engineering Data Mining

Abstract

Development of software is a very crucial and complex process. IT professionals have been facing difficulties and risks for the development of software projects. This research focuses on the effectiveness of two data mining algorithms that are Apriori algorithm and FP-Tree Algorithm in Software Engineering domain. These two algorithms have shown the capability of generating association rules in software engineering data. Associations between software risk factors and risk mitigation have been found using these algorithms which is a unique idea. Both the algorithms have different methodologies but with the same goals in the form of rules generation. This research paper targeted three things. Tracing of these two algorithms applied to the given dataset, Comparison in terms of pros and cons of both the algorithms and the combination of the two, novel adaptive architecture in a clear way. Embedding data mining techniques in software engineering have shown spark and generates fruitful results.