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During the software system’s maintenance and evolution, finding and removing software bugs is a very important part that consumes a large amount of money and effort. To analyze different bugs’ character, it is very essential to know how long or which period of versions does the bug live in. In this study, we define version-based bug lifecycle and propose a text features based classification model to predict the versionlength of bug lifecycle. We collect 57000+ bugs from 10 well-know Apache Software Foundation projects to construct our dataset, and use the tf-idf method to collect our text features from bug report’s summary and description. Our experimental results show that the text feature based method performs better than other baseline methods on 10 projects. The text feature based Naive Bayes classifiers outperforms all other methods with different features and classifiers.

How Many Versions does a Bug Live in? An Empirical Study on Text Features for Bug Lifecycle Prediction