Hyperparameter Optimization for AST Differencing
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
Computing the differences between two versions of the same program is an essential task for software development and software evolution research. AST differencing is the most advanced way of doing so, and an active research area. Yet, AST differencing algorithms rely on configuration parameters that may have a strong impact on their effectiveness. In this paper, we present a novel approach named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAT</monospace> (D <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iff <u>A</u>uto <u>T</u>uning</i> ) for hyperparameter optimization of AST differencing. We thoroughly state the problem of hyper-configuration for AST differencing. We evaluate our data-driven approach <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAT</monospace> to optimize the edit-scripts generated by the state-of-the-art AST differencing algorithm named GumTree in different scenarios. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAT</monospace> is able to find a new configuration for GumTree that improves the edit-scripts in 21.8% of the evaluated cases.
Results and benchmarks
Computing the differences between two versions of the same program is an essential task for software development and software evolution research.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 60/100, grounding 58/100, status medium.
Implementation
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Time to first repro: a few hours
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Reproduction readiness
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Validation caveat
Hugging Face artifacts
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Models
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Research context
13
Citations
73
References
Tasks
Scripting language, Computer science, Hyperparameter, Software, Task (project management), Programming language, Information Systems
Methods
Algorithm
Domains
Artificial intelligence
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