Improving the Flexibility of CLARA\u27s Automated Matching and Repair Processes

More computer science researchers focus on automated program repair, with the world steadfastly moving towards automation. CLARA is an example of an automated program repair tool that provides feedback to novice programmers solving introductory programming assignments in Java, C++, and Python. CLARA involves test-based repair, requiring as input a correct program, an incorrect program, and its corresponding test case. Our work only focuses on Python. CLARA has two main limitations. The first involves lacking support for commonly used language constructs such as standard input, standard output, and import statements. We address this issue by extending CLARA\u27s abstract syntax tree processor and interpreter to include these constructs. The second limitation is that CLARA requires both the correct and the incorrect program to have the same control flow. In a real-world setting, it is not easy to find such programs, reducing the true impact CLARA can have on the learning of novice programmers. Therefore, we implement a graph matching technique between the correct and incorrect programs that considers both the semantic and the topological information to help overcome this limitation. Using this matching, we modify the incorrect program to match its control flow with the correct program. To verify that our technique overcomes the control flow limitation, we conduct experiments to run CLARA and compare the number of programs repaired with and without the graph matching technique. We also analyze the percentage of the program modified by CLARA and the number of correct programs needed to repair all valid incorrect programs. Our experiments show that CLARA can parse, process, and repair many more programs after our extensions. Additionally, our experiments indicate that we never enable CLARA to replace all the source code of an incorrect program with all the source code of a correct program