To this end, we develop a novel ASE method namedRepoUnderstander by guiding agents to comprehensively under-stand the whole repositories. Specifically, we first condense thecritical information of the whole repository into the repositoryknowledge graph in a top-to-down mode to decrease the complex-ity of repository. Subsequently, we empower the agents the abilityof understanding whole repository by proposing a Monte Carlotree search based repository exploration strategy. In addition, tobetter utilize the repository-level knowledge, we guide the agents tosummarize, analyze, and plan. Then, they can manipulate the toolsto dynamically acquire information and generate the patches tosolve the real-world GitHub issues.

To identify the essential code elements needed to com-plete the given infilling method m in a repository, a naivesolution might scan the entire codebase for all accessibleelements, which would introduce excessive noise. Anotherapproach could focus on methods with similar signatures orcontexts; however, these often provide irrelevant elementsthat do not serve m’s functional purpose, leading to redun-dancy and missing critical elements.

pruning the specific implementations of func-tions in all dependent files does not signifi-cantly reduce the accuracy of completions

Greedy Selection. Retrieval is performed if<cc> is the most likely token following <eof>.• Threshold Selection. If the probability of <cc>