  Context: Recent research has used data mining to develop techniques that can
guide developers through source code changes. To the best of our knowledge,
very few studies have investigated data mining techniques and--or compared
their results with other algorithms or a baseline. Objectives: This paper
proposes an automatic method for recommending source code changes using four
data mining algorithms. We not only use these algorithms to recommend source
code changes, but we also conduct an empirical evaluation. Methods: Our
investigation includes seven open-source projects from which we extracted
source change history at the file level. We used four widely data mining
algorithms \ie{} Apriori, FP-Growth, Eclat, and Relim to compare the algorithms
in terms of performance (Precision, Recall and F-measure) and execution time.
Results: Our findings provide empirical evidence that while some Frequent
Pattern Mining algorithms, such as Apriori may outperform other algorithms in
some cases, the results are not consistent throughout all the software
projects, which is more likely due to the nature and characteristics of the
studied projects, in particular their change history. Conclusion: Apriori seems
appropriate for large-scale projects, whereas Eclat appears to be suitable for
small-scale projects. Moreover, FP-Growth seems an efficient approach in terms
of execution time.

Software Engineering;Artificial Intelligence;Machine Learning