An Analysis of the Search Spaces for Generate and Validate Patch Generation Systems (1602.05643v1)

Abstract: We present the first systematic analysis of the characteristics of patch search spaces for automatic patch generation systems. We analyze the search spaces of two current state-of-the-art systems, SPR and Prophet, with 16 different search space configurations. Our results are derived from an analysis of 1104 different search spaces and 768 patch generation executions. Together these experiments consumed over 9000 hours of CPU time on Amazon EC2. The analysis shows that 1) correct patches are sparse in the search spaces (typically at most one correct patch per search space per defect), 2) incorrect patches that nevertheless pass all of the test cases in the validation test suite are typically orders of magnitude more abundant, and 3) leveraging information other than the test suite is therefore critical for enabling the system to successfully isolate correct patches. We also characterize a key tradeoff in the structure of the search spaces. Larger and richer search spaces that contain correct patches for more defects can actually cause systems to find fewer, not more, correct patches. We identify two reasons for this phenomenon: 1) increased validation times because of the presence of more candidate patches and 2) more incorrect patches that pass the test suite and block the discovery of correct patches. These fundamental properties, which are all characterized for the first time in this paper, help explain why past systems often fail to generate correct patches and help identify challenges, opportunities, and productive future directions for the field.

Analysis of Patch Search Spaces in Generate and Validate Systems

The paper "An Analysis of the Search Spaces for Generate and Validate Patch Generation Systems" provides a comprehensive paper of patch search spaces utilized by automatic patch generation systems, particularly focusing on SPR and Prophet. This paper presents significant insights into how the structure and configuration of these search spaces can impact the effectiveness and success of the patch generation process.

Key findings from the analysis include several revelations about patch occurrence and system effectiveness:

1. Patch Sparsity: The paper reveals that correct patches are sparse within the search spaces, often limited to a single correct patch per search space per defect. Out of the considered defects, 45 have no correct patches in any of the evaluated spaces, and many have only one correct patch in the few spaces that contain them.
2. Abundance of Plausible Patches: In contrast to the scarcity of correct patches, plausible but incorrect patches occur in significantly greater numbers. This abundance poses challenges in isolating correct patches, as these incorrect patches can mislead the validation process when they pass the test suites used for evaluation.
3. Prophet vs. SPR Prioritization: The paper highlights the effectiveness of Prophet, a system that leverages machine learning for patch prioritization, in isolating correct patches more rapidly than SPR, which relies on heuristic strategies. Both systems outperform random search strategies, which reinforces the importance of intelligent prioritization in the patch finding process.
4. Search Space Tradeoffs: A complex dynamic between search space size and system performance is identified. While larger and richer patch spaces can theoretically contain correct patches for a broader set of defects, they may complicate the identification process due to increased validation overhead and the proliferation of plausible but incorrect patches that might block correct patch discovery.

The implications of these findings are multifaceted:

• System Performance Contextualization: The results contextualize the earlier performance of systems like GenProg, AE, and RSRepair, which often struggle with similar issues of sparse correct patches overwhelmed by incorrect ones. These systems need strategies for prioritizing patches effectively and mitigating the influence of weak test suites.
• Enhanced Patch Prioritization Strategies: The success of Prophet suggests a promising direction involving the use of machine learning models pre-trained on human-correct patches to guide prioritization. This approach can help navigate and streamline the exploration of complex patch spaces by spotlighting potentially successful patches early in the process.
• Future Directions: Generate and validate systems may advance by incorporating alternative sources of information beyond standard test suites. Besides machine learning, methodologies such as invariant-based patching (as employed by ClearView) or examining extensive code repositories for correct patch synthesis might improve scalability and patch accuracy.

The paper underscores the challenges faced by current patch generation systems in navigating large and complex patch spaces and draws attention to the role of test suite strength and non-test suite information in improving patch discovery. A focus on these aspects holds potential for enhancing future systems' ability to autonomously generate reliable and correct patches across increasingly broad defect categories.