Streamlining Software Reviews: Efficient Predictive Modeling with Minimal Examples

TL;DR

This paper introduces a novel approach for software review using predictive models trained on minimal examples, enabling efficient decision-making and automation in software analysis tasks.

Contribution

It demonstrates that effective predictive models for software review can be built with as few as 12 to 30 labeled examples, a significant reduction in data requirements.

Findings

Models trained with minimal data perform well across diverse case studies.

The approach reduces SME effort in software review processes.

Open-source code and data are provided for reproducibility.

Abstract

This paper proposes a new challenge problem for software analytics. In the process we shall call "software review", a panel of SMEs (subject matter experts) review examples of software behavior to recommend how to improve that's software's operation. SME time is usually extremely limited so, ideally, this panel can complete this optimization task after looking at just a small number of very informative, examples. To support this review process, we explore methods that train a predictive model to guess if some oracle will like/dislike the next example. Such a predictive model can work with the SMEs to guide them in their exploration of all the examples. Also, after the panelists leave, that model can be used as an oracle in place of the panel (to handle new examples, while the panelists are busy, elsewhere). In 31 case studies (ranging from from high-level decisions about software processes to low-level decisions about how to configure video encoding software), we show that such predictive models can be built using as few as 12 to 30 labels. To the best of our knowledge, this paper's success with only a handful of examples (and no large language model) is unprecedented. In accordance with the principles of open science, we offer all our code and data at https://github.com/timm/ez/tree/Stable-EMSE-paper so that others can repeat/refute/improve these results.