Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: Co-Change and Tarmaq. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.

Association rule mining is an unsupervised learning technique that infers relationships among items in a data set. This technique has been successfully used to analyze a system's change history and uncover evolutionary coupling between system artifacts. Evolutionary coupling can, in turn, be used to recommend artifacts that are potentially affected by a given set of changes to the system. In general, the quality of such recommendations is affected by (1) the values selected for various parameters of the mining algorithm, (2) characteristics of the set of changes used to derive a recommendation, and (3) characteristics of the system's change history for which recommendations are generated.

Past research has proposed association rule mining as a means to uncover the evolutionary coupling from a system's change history. These couplings have various applications, such as improving system decomposition and recommending related changes during development. The strength of the coupling can be characterized using a variety of interestingness measures. Existing recommendation engines typically use only the rule with the highest interestingness value in situations where more than one rule applies. In contrast, we argue that multiple applicable rules indicate increased evidence, and hypothesize that the aggregation of such rules can be exploited to provide more accurate recommendations.

The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files) potentially affected by a change. Recently, there is an increased interest in mining software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system's change history. Two main considerations when using this approach are the history length, the number of transactions from the change history used to identify the impact of a change, and history age, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters. In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary couplings. Specifically, we report on a series of systematic experiments involving the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis.

Software change impact analysis aims to find artifacts potentially affected by a change. Typical approaches apply language-specific static or dynamic dependence analysis, and are thus restricted to homogeneous systems. This restriction is a major drawback given today's increasingly heterogeneous software. Evolutionary coupling has been proposed as a language-agnostic alternative that mines relations between source-code entities from the system's change history. Unfortunately, existing evolutionary coupling based techniques fall short. For example, using Singular Value Decomposition (SVD) quickly becomes computationally expensive. An efficient alternative applies targeted association rule mining, but the most widely known approach (ROSE) has restricted applicability: experiments on two large industrial systems, and four large open source systems, show that ROSE can only identify dependencies about 25% of the time. To overcome this limitation, we introduce TARMAQ, a new algorithm for mining evolutionary coupling. Empirically evaluated on the same six systems, TARMAQ performs consistently better than ROSE and SVD, is applicable 100% of the time, and runs orders of magnitude faster than SVD. We conclude that the proposed algorithm is a significant step forward towards achieving robust change impact analysis for heterogeneous systems.

Regression testing is an essential step in safeguarding the evolution of a system, yet there is often not enough time to exercise all available tests. Identifying the subset of tests that can reveal potential issues introduced by a change is a challenge. It requires identifying the tests that test the chan- ged part of the software. Furthermore, and more challeng- ing, it requires identifying the parts of the system that are potentially affected by that change, a task typically done by means of static program analysis. In this doctoral research, we investigate an alternative approach, using software repos- itory mining. We propose a method that mines the change history of a system to uncover dependencies, and uses these for test-selection and test-prioritization. By reducing the amount of test to exercise, and limiting time spend on test- plan creation (i.e., selecting and prioritizing tests), the aim of our approach is to increase cost-effectiveness of software regression testing.