Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis

Pan, Rangeet; Le, Vu; Nagappan, Nachiappan; Gulwani, Sumit; Lahiri, Shuvendu K.; Kaufman, Mike

doi:10.1109/icse43902.2021.00077

Cited by 17 publications

(25 citation statements)

References 26 publications

(33 reference statements)

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“…Some other studies characterize the root causes and/or resolutions of textual conlicts [18,29,37,50,53,58]. Speciically, Ji et al studied the textual conlicts caused by Git RebaseÐa practice to merge program changes by rewriting the evolution history [37].…”

Section: Empirical Studies On Merge Conflictsmentioning

confidence: 99%

“…They observed that 44% of conlicts were caused by conlicting updates to the same line of code, and developers resolved 99% of conlicts by taking either the left-or right-version of code. Pan et al studied 271 merging scenarios with textual conlicts inside the repository of Microsoft Edge [53]. The researchers clustered scenarios based on the ile types, conlict sizes, conlict locations, and resolution patterns.…”

Section: Empirical Studies On Merge Conflictsmentioning

confidence: 99%

See 1 more Smart Citation

A Characterization Study of Merge Conflicts in Java Projects

Shen

Gulzar

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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In collaborative software development, programmers create software branches to add features and fix bugs tentatively, and then merge branches to integrate edits. When edits from different branches textually overlap (i.e., textual conflicts ) or lead to compilation and runtime errors (i.e., build and test conflicts ), it is challenging for developers to remove such conflicts. Prior work proposed tools to detect and solve conflicts. They investigate how conflicts relate to code smells and the software development process. However, many questions are still not fully investigated, such as what types of conflicts exist in real-world applications and how developers or tools handle them. For this paper, we used automated textual merge, compilation, and testing to reveal 3 types of conflicts in 208 open-source repositories: textual conflicts, build conflicts (i.e., conflicts causing build errors), and test conflicts (i.e., conflicts triggering test failures). We manually inspected 538 conflicts and their resolutions to characterize merge conflicts from different angles. Our analysis revealed three interesting phenomena. First, higher-order conflicts (i.e., build and test conflicts) are harder to detect and resolve, while existing tools mainly focus on textual conflicts. Second, developers manually resolved most higher-order conflicts by applying similar edits to multiple program locations; their conflict resolutions share common editing patterns implying great opportunities for future tool design. Third, developers resolved 64% of true textual conflicts by keeping complete edits from either a left or right branch. Unlike prior studies, our research for the first time thoroughly characterizes three types of conflicts, with a special focus on higher-order conflicts and limitations of existing tool design. Our work will shed light on future research of software merge.

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Section: Empirical Studies On Merge Conflictsmentioning

confidence: 99%

Section: Empirical Studies On Merge Conflictsmentioning

confidence: 99%

A Characterization Study of Merge Conflicts in Java Projects

Shen

Gulzar

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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“…They do not automate the task of synthesizing a rule from a cluster of code changes. Program synthesis has also been recently applied to other code related applications such as API migration [Gao et al 2021;Ni et al 2021], synthesis of merge conflict resolutions [Pan et al 2021], interactive code search ]. Datalog synthesis: There is a rich body of recent work on datalog synthesis [Albarghouthi et al 2017;Si et al 2018Si et al , 2019Thakkar et al 2021] and its application to code related tasks such as interactive code search .…”

Section: Related Workmentioning

confidence: 99%

Example-based Synthesis of Static Analysis Rules

Garg¹,

SHS²

2022

Preprint

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Static Analysis tools have rules for several code quality issues and these rules are created by experts manually. In this paper, we address the problem of automatic synthesis of code quality rules from examples. We formulate the rule synthesis problem as synthesizing first order logic formulas over graph representations of code. We present a new synthesis algorithm RhoSynth that is based on Integer Linear Programming-based graph alignment for identifying code elements of interest to the rule. We bootstrap RhoSynth by leveraging code changes made by developers as the source of positive and negative examples. We also address rule refinement in which the rules are incrementally improved with additional user-provided examples. We validate RhoSynth by synthesizing more than 30 Java code quality rules. These rules have been deployed as part of a code review system in a company and their precision exceeds 75% based on developer feedback collected during live code-reviews. Through comparisons with recent baselines, we show that current state-of-the-art program synthesis approaches are unable to synthesize most of these rules.

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“…Gulwani [20] introduced FlashFill to synthesize string transformation scripts from examples. In the software engineering domain, there have been efforts to apply program synthesis techniques on code related tasks such as learning version update patches [5], code edit scripts [43], and merge conflict resolutions [52]. However, our work targets a new domain that has not been explored using PBE: software documentation.…”

Section: Related Workmentioning

confidence: 99%

AutoTSG: Learning and Synthesis for Incident Troubleshooting

Shetty¹,

Bansal²,

Upadhyayula³

et al. 2022

Preprint

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Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to be used by on-call engineers (OCEs). However, TSGs are siloed, unstructured, and often incomplete, requiring developers to manually understand and execute necessary steps. This results in a plethora of issues such as on-call fatigue, reduced productivity, and human errors. In this work, we conduct a large-scale empirical study of over 4K+ TSGs mapped to 1000s of incidents and find that TSGs are widely used and help significantly reduce mitigation efforts. We then analyze feedback on TSGs provided by 400+ OCEs and propose a taxonomy of issues that highlights significant gaps in TSG quality. To alleviate these gaps, we investigate the automation of TSGs and propose AutoTSG -a novel framework for automation of TSGs to executable workflows by combining machine learning and program synthesis. Our evaluation of AutoTSG on 50 TSGs shows the effectiveness in both identifying TSG statements (accuracy 0.89) and parsing them for execution (precision 0.94 and recall 0.91). Lastly, we survey ten Microsoft engineers and show the importance of TSG automation and the usefulness of AutoTSG.

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Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis

Cited by 17 publications

References 26 publications

A Characterization Study of Merge Conflicts in Java Projects

A Characterization Study of Merge Conflicts in Java Projects

Example-based Synthesis of Static Analysis Rules

AutoTSG: Learning and Synthesis for Incident Troubleshooting

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