Mining Patterns in Source Code Using Tree Mining Algorithms

Abstract

Discovering regularities in source code is of great interest to software engineers, both in academia and in industry, as regularities can provide useful information to help in a variety of tasks such as code comprehension, code refactoring, and fault localisation. However, traditional pattern mining algorithms often find too many patterns of little use and hence are not suitable for discovering useful regularities. In this paper we propose FREQTALS, a new algorithm for mining patterns in source code based on the FREQT tree mining algorithm. First, we introduce several constraints that effectively enable us to find more useful patterns; then, we show how to efficiently include them in FREQT. To illustrate the usefulness of the constraints we carried out a case study in collaboration with software engineers, where we identified a number of interesting patterns in a repository of Java code.

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

“…At the end of the semester, the received source codes are analyzed using intelligent algorithms in order to identify the most common approaches used by students while solving the unique programming exercises [24,25]. Several methods exist that allow mining idioms from source codes [26,27]. The aim of the current research is the identification of the knowledge gaps of students from different groups and departments in order to explain the least frequently used language features of the programming language to students before the final test [25].…”

Pass/Fail Prediction in Programming Courses

“…The context used could be expanded, although the important caveat here is that the method still needs to maintain sufficient performance. We could also consider applying some of the source code pattern mining techniques proposed by Pham et al (2019) to achieve further speed improvements. This could help with the outliers seen in the timing data.…”

“…Program code can be represented as an abstract syntax tree (AST), where the nodes of the tree represent the language constructs used in the program. Recent work has demonstrated the efficacy of this approach in efficiently identifying frequent patterns in source code (Pham et al, 2019). In an educational context, these techniques have already been used to find patterns common to solutions that failed a given exercise (Mens et al, 2021).…”