Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction

By Beat Fluri, Michael Würsch, Martin Pinzger, Harald C. Gall. IEEE Transactions on Software Engineering, 2007

665 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34c6dbcd1af19f033f1e436a8391d0cc9db2a489

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Citations in the corpus

Differential symbolic execution (2008, background): “Several researchers have explored the use of an abstract syntax tree to compare program versions [26, 51].”
Accurate and Efficient Refactoring Detection in Commit History (2018, methodology): “The body of a method is represented as a tree capturing the nesting structure of the code, where each node corresponds to a statement, similar to the representation used by Fluri et al. [23].”
A Large-Scale Study on Repetitiveness, Containment, and Composability of Routines in Open-Source Projects (2016, neutral): “The typical categories are text-based [16, 38], token-based [9, 27, 34, 42], tree-based [11, 25, 18], and graph-based [31, 36].”
Fine-grained and accurate source code differencing (2014, background): “Despite several key contributions (e.g. [13]), the problem of computing AST edit scripts is still open, with two main challenges: handling move actions, and scaling to ﬁne-grained ASTs with thousands of nodes 1 .”
Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns (2019, methodology): “Second, we conducted a comparative study to evaluate our tool against the syntax-based technique used in the existing approaches [7], [9], [22], [23], [25].”
Shaping program repair space with existing patches and similar code (2018, methodology): “ChangeDistiller [10] is a widely-used approach for source code transformation at AST level.”
DeepDelta: learning to repair compilation errors (2019, methodology): “To analyze code changes at the syntactic level, we take a tree dierencing approach [13, 15].”
When Old Facts Return: Re-Reads, Reverts, and the Limits of Temporal Memory (2026, unknown): “Fine-grained source diﬀerencing can identify syntactic changes (Fluri et al., 2007; Falleri et al., 2014); contract-oriented representations can describe selected interface changes (Meyer, 1992).”