Combining Example-Based and Rule-Based Program Transformations to Resolve Build Conflicts

TL;DR

This paper presents BuCoR, a hybrid conflict resolution tool combining rule-based and example-based strategies to effectively resolve build conflicts in software merging scenarios, improving automation and accuracy.

Contribution

It introduces BuCoR, a novel conflict resolver that integrates rule-based and example-based transformations to address diverse build conflicts in software development.

Findings

Successfully resolved 34 out of 88 real-world conflicts

Generated solutions for 65 conflicts across 21 conflict types

Demonstrated effectiveness of hybrid approach in conflict resolution

Abstract

Merge conflicts often arise when developers integrate changes from different software branches. The conflicts can result from overlapping edits in programs (i.e., textual conflicts) or cause build and test errors (i.e., build and test conflicts). They degrade software quality and hinder programmer productivity. While several tools detect build conflicts, few offer meaningful support for resolving them. To overcome limitations of existing tools, we introduce BuCoR (Build Conflict Resolver), a new conflict resolver. BuCoR first detects conflicts by comparing three versions related to a merging scenario: base b, left l, and right r. To resolve conflicts, it employs two complementary strategies: example-based transformation (BuCoR-E) and rule-based transformation (BuCoR-R). BuCoR-R applies predefined rules to resolve conflicts in frequently suggested or conventional ways. BuCoR-E mines branch versions (l and r) for exemplar edits applied to fix related build errors. From these examples, it infers and generalizes program transformation patterns to resolve conflicts in project-specific or unconventional ways. We evaluated BuCoR on 88 real-world build conflicts spanning 21 distinct conflict types. BuCoR generated at least one solution for 65 cases and correctly resolved 34 conflicts. We observed that this hybrid approach--combining context-aware, example-based learning with structured, rule-based resolution--can effectively help resolve conflicts. Our research sheds light on future directions for more intelligent and automated merge tools.