Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149074
Title: Precise semantic history slicing through dynamic delta refinement
Authors: Li, Yi
Zhu, Chenguang
Gligoric, Milos
Rubin, Julia
Chechik, Marsha
Keywords: Engineering::Computer science and engineering::Software::Software engineering
Issue Date: 2019
Source: Li, Y., Zhu, C., Gligoric, M., Rubin, J. & Chechik, M. (2019). Precise semantic history slicing through dynamic delta refinement. Automated Software Engineering, 26(4), 757-793. https://dx.doi.org/10.1007/s10515-019-00260-8
Project: MOE Tier1 2018-T1-002-069
Journal: Automated Software Engineering
Abstract: Semantic history slicing solves the problem of extracting changes related to a particular high-level functionality from software version histories. State-of-the-art techniques combine static program analysis and dynamic execution tracing to infer an over-approximated set of changes that can preserve the functional behaviors captured by a test suite. However, due to the conservative nature of such techniques, the sliced histories may contain irrelevant changes. In this paper, we propose a divide-and-conquer-style partitioning approach enhanced by dynamic delta refinement to produce much smaller semantic history slices. We utilize deltas in dynamic invariants generated from successive test executions to learn significance of changes with respect to the target functionality. Additionally, we introduce a file-level commit splitting technique for untangling unrelated changes introduced in a single commit. Empirical results indicate that these measurements accurately rank changes according to their relevance to the desired test behaviors and thus partition history slices in an efficient and effective manner.
URI: https://hdl.handle.net/10356/149074
ISSN: 0928-8910
DOI: 10.1007/s10515-019-00260-8
Rights: © 2019 Springer Science+Business Media. This is a post-peer-review, pre-copyedit version of an article published in Automated Software Engineering. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10515-019-00260-8
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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