In this paper, a multi label variant of CLUBAS [1] algorithm, ML-CLUBAS (Multi Label-CLassification of software Bugs Using Bug Attribute Similarity) is presented. CLUBAS is a hybrid algorithm, and is designed by using text clus-tering, frequent term calculations and taxonomic terms mapping techniques, and is an example of classification using clustering technique. CLUBAS is a single label algorithm, where one bug cluster is exactly mapped to a single bug

Abstract — Over the last decade web applications are becoming very popular. These are becoming more users oriented now days. Various languages used for the development of a web application like PHP, Java, ASP.NET etc. Development of a web application is not done by individual; it is a result of team’s efforts. Different type of bugs and errors are present in source code. Finding out these bugs or errors is a difficult task. Deep understanding of the language is required to detect bugs or errors in source code. Different tools are used to check the accuracy of the source code. There is a need to classify these detected bugs so that fewer efforts are done for selecting the individual to correct these. To detect and classify these manually is a time consuming task. This paper has presented the combination of Software Engineering with Data Mining Techniques. Aim of this work is to detect and classify the bugs