There are many software systems that have been used and maintained for a long time. By undergoing such a maintenance process, similar code fragments were intentionally left in the source code of such software, and knowing how to manage a software system that contains a lot of similar code fragments becomes a major concern. In this study, we proposed a method to pick up components that were commonly used in similar code fragments from a target software system. This method was realized by using the component rank model and by checking the differences of evaluation values for each component before and after merging components that had similar code fragments. In many cases, components whose evaluation value had decreased would be used by both the components that were merged, so we considered that these components were commonly used in similar code fragments. Based on the proposed approach, we implemented a system to calculate differences of evaluation values for each component, and conducted three evaluation experiments to confirm that our method was useful for detecting components that were commonly used in similar code fragments, and to confirm how our method can help developers when developers add similar components. Based on the experimental results, we also discuss some improvement methods and provide the results from applications of these methods.

Software developers may write a number of similar source code fragments including the same mistake in software products. To remove such faulty code fragments, developers inspect code clones if they found a bug in their code. While various code clone detection methods have been proposed to identify clones of either code blocks or functions, those tools do not always fit the code inspection task because a faulty code fragment may be much smaller than code blocks, e.g. a single line of code. To enable developers to search code clones of such a small faulty code fragment in a large-scale software product, we propose a method using Lempel-Ziv Jaccard Distance, which is an approximation of Normalized Compression Distance. We conducted an experiment using an existing research dataset and a user survey in a company. The result shows our method efficiently reports cloned faulty code fragments and the performance is acceptable for software developers.