Metarule-guided association rule mining for program understanding

Abstract

Software systems are expected to change over their lifetime in order to remain useful. Understanding a software system that has undergone changes is often difficult owing to the unavailability of up-to-date documentation. Under these circumstances, source code is the only reliable means of information regarding the system. In the paper, association rule mining is applied to the problem of software understanding i.e. given the source files of a software system, association rule mining is used to gain an insight into the software. To make association rule mining more effective, constraints are placed on the mining process in the form of metarules. Metarule-guided mining is carried out to find associations which can be used to identify recurring problems within software systems. Metarules are related to re-engineering patterns which present solutions to these problems. Association rule mining is applied to five legacy systems and results presented show how extracted association rules can be helpful in analysing the structure of a software system and modifications to improve the structure are suggested. A comparison of the results obtained for the five systems also reveals legacy system characteristics, which can lead to understanding the nature of open source legacy software and its evolution.