Elixir: Effective object-oriented program repair (2112.10915v1)

Abstract: This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.

• The paper introduces Elixir, a novel technique for object-oriented program repair that expands the repair search space to include method calls and uses a machine learning model to rank candidate patches.
• Elixir achieved significant performance gains, increasing correct repairs by 85% on Defects4J and 57% on Bugs.jar compared to baseline techniques, outperforming other state-of-the-art tools.
• Elixir's approach, effective in addressing method-invocation bugs, can be adapted to other OO languages and suggests future directions for integrating machine learning and other techniques in program repair.

A Review of "Elixir: Effective Object Oriented Program Repair"

In the domain of automated program repair, the paper titled "Elixir: Effective Object Oriented Program Repair" presents a targeted approach to enhancing the repair of object-oriented (OO) programs, particularly focusing on Java. Recognizing the ubiquity of method invocations in OO programs and the corresponding prevalence of these invocations in bug patches, the authors propose a generate-and-validate technique called Elixir that seeks to expand the repair expressions by aggressively incorporating method calls.

Technical Approach

Elixir differentiates itself by significantly enlarging the repair search space to include richer repair expressions, which encompass method calls alongside local variables and constants as primary building blocks. The resulting challenge—managing the expanded search space—is adeptly addressed by leveraging a machine-learned model to rank and prioritize repair candidates. This model utilizes four pragmatically chosen features that account for contextual and bug report similarities, among other factors. The combination of an expanded search space with a robust ranking mechanism enables Elixir to explore more expressive candidate patches effectively.

The authors evaluate Elixir on two datasets, Defects4J and a newly curated dataset, Bugs.jar. The results are notable: Elixir achieves an 85% increase in correct bug repairs on Defects4J and a 57% increase on Bugs.jar compared to baseline techniques. Furthermore, it significantly surpasses other state-of-the-art tools such as ACS, HD-Repair, and NOPOL in terms of performance. This empirical success underscores Elixir's capability to handle the specific nuances and requirements of OO-programs in Java, particularly involving method invocation bugs.

Implications and Future Directions

The practical contribution of Elixir lies in its enhanced ability to generate correct patches by addressing method-invocation-related bugs—a common yet complex problem in real-world OO-programs. The method goes beyond existing frameworks by using an expansive set of repair templates that are intelligently sifted through by its ranking model, demonstrating an effective amalgamation of AI-driven insights and traditional program repair methodologies.

From a theoretical standpoint, Elixir’s methodology introduces a framework that could be adapted to other OO-languages beyond Java, such as C++. The use of machine learning for prioritizing repair candidates marks a forward-looking approach that could inspire further innovation in using AI in program repair tasks. Future work could explore the potential integration of Elixir with other complementary techniques, such as historical patch-based approaches, for even more enhanced repairs.

The paper's results invite future investigations into refining the machine learning features and increasing dataset diversity, thus potentially extending Elixir's applicability and robustness across a broader spectrum of programming environments. Furthermore, exploring synergies between Elixir and other program repair tools, such as ACS, could lead to a more comprehensive solution, tailoring fixes not only for conditional statement errors but also for other common bug patterns within OO-codebases.

In summary, "Elixir: Effective Object Oriented Program Repair" eloquently provides a substantial contribution to the field of automated program repair through its integration of machine learning models to rank and validate an expanded ensemble of candidate patches. It showcases a significant step forward in addressing the complex nature of repairs in object-oriented programs, setting a precedent for future research in automated repair systems bolstered by AI technologies.