# Explainable Software Bot Contributions: Case Study of Automated Bug Fixes

## TL;DR

This paper explores the importance of explainability in automated software contributions, proposing that bots should provide not only fixes but also explanations and context to be accepted by human developers.

## Contribution

It introduces the concept of explainable software bot contributions, emphasizing the need for context and explanations in automated bug fixes, based on a case study with the Repairnator bot.

## Key findings

- Dry patches often ignored due to lack of explanation
- Explainable bug fixes include patches, explanations, and behavioral highlights
- Proposes a framework for integrating explanations into automated contributions

## Abstract

In a software project, esp. in open-source, a contribution is a valuable piece of work made to the project: writing code, reporting bugs, translating, improving documentation, creating graphics, etc. We are now at the beginning of an exciting era where software bots will make contributions that are of similar nature than those by humans. Dry contributions, with no explanation, are often ignored or rejected, because the contribution is not understandable per se, because they are not put into a larger context, because they are not grounded on idioms shared by the core community of developers. We have been operating a program repair bot called Repairnator for 2 years and noticed the problem of "dry patches": a patch that does not say which bug it fixes, or that does not explain the effects of the patch on the system. We envision program repair systems that produce an "explainable bug fix": an integrated package of at least 1) a patch, 2) its explanation in natural or controlled language, and 3) a highlight of the behavioral difference with examples. In this paper, we generalize and suggest that software bot contributions must explainable, that they must be put into the context of the global software development conversation.