# SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

arXiv: https://arxiv.org/abs/2102.09687 · HTML: https://mlsanity.com/paper/2102.09687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 71 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 14 of 14)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models](https://mlsanity.com/paper/1801.10269/) (2018)
- [Explanations of model predictions with live and breakDown packages](https://mlsanity.com/paper/1804.01955/) (2018)
- [Predicting Defective Lines Using a Model-Agnostic Technique](https://mlsanity.com/paper/2009.03612/) (2020)
- [LoRMIkA: Local rule-based model interpretability with k-optimal associations](https://mlsanity.com/paper/1908.03840/) (2019)
- [Explainable AI for Software Engineering](https://mlsanity.com/paper/2012.01614/) (2020)
- [Applications of Psychological Science for Actionable Analytics](https://mlsanity.com/paper/1803.05067/) (2018)
- [AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models](https://mlsanity.com/paper/1806.09791/) (2018)
- [The Impact of Correlated Metrics on Defect Models](https://mlsanity.com/paper/1801.10271/) (2018)
- [Assessing the Students' Understanding and their Mistakes in Code Review Checklists -- An Experience Report of 1,791 Code Review Checklist Questions from 394 Students](https://mlsanity.com/paper/2101.04837/) (2021)

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data](https://mlsanity.com/paper/2202.07766/) (2022)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)
- [Mining Action Rules for Defect Reduction Planning](https://mlsanity.com/paper/2405.13740/) (2024)

## Abstract (arXiv)

Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, defect prediction models have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable-i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners' perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rule-based explanations for the predictions of defect prediction models. Finally, we develop and evaluate an information visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80% of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.