The Impact of Automated Parameter Optimization on Defect Prediction Models (1801.10270v1)

Abstract: Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28% of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.

• The paper shows that automated optimization can boost defect prediction performance, improving AUC by up to 40 percentage points for classifiers like C5.0, neural networks, and CART.
• The paper finds that optimized models exhibit enhanced stability and altered variable importance rankings, with only 28% of features retaining their original rank.
• The paper demonstrates that many sensitive parameters remain optimal across similar datasets and that the low computational cost of grid search makes optimization practical.

The Impact of Automated Parameter Optimization on Defect Prediction Models

The paper "The Impact of Automated Parameter Optimization on Defect Prediction Models", published in IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, investigates the effects of automated parameter optimization on the performance, stability, and interpretability of software defect prediction models. This paper is significant since, traditionally, defect prediction models are often used with default parameter settings, which recent studies suggest may not yield optimal performance.

Numerical Results and Key Findings

The researchers conducted an empirical paper utilizing 18 datasets to explore how automated parameter optimization influences defect prediction models, with notable findings:

1. Performance Improvement: Automated optimization enhanced the AUC (Area Under Curve) of defect prediction models by up to 40 percentage points, especially benefiting techniques like C5.0, neural networks, and CART. Conversely, widely-used random forest classifiers showed negligible improvements, stressing the need for researchers to carefully consider parameter settings based on the chosen classification techniques.
2. Stability and Interpretation: Optimized classifiers demonstrated better or equal stability compared to defaults in 35% of the techniques studied. Moreover, optimization significantly affected model interpretation, shifting importance ranking of variables, with as few as 28% maintaining their rank. This underscores the substantial impact of parameter settings on the insights derived from defect prediction models.
3. Parameter Transferability: The paper found that 17 out of 20 sensitive parameters maintained optimal settings across datasets with similar metrics without significant performance drops. However, certain classifiers, such as LogitBoost and FDA, necessitated dataset-specific optimization.
4. Computational Cost: The cost of applying grid search optimization added less than 30 minutes of additional computation for 46% of the classification techniques, demonstrating its feasibility in practical applications. This cost, translated to less than one US dollar using Amazon EC2 estimates, shows the practicality of performing such optimizations even with budget constraints.
5. Ranking of Classification Techniques: Interestingly, the paper challenges previous notions by revealing that after optimization, less commonly used techniques like C5.0 often outperformed popular choices like random forests across datasets. This highlights the criticality of parameter exploration in classification techniques.

Implications and Future Considerations

The implications of the paper are multifaceted, impacting both practical and theoretical realms. Practically, the merits of automated parameter optimization suggest software practitioners and researchers should incorporate this approach to improve the accuracy and robustness of defect prediction models. This involves moving away from reliance on default parameters and instead leveraging optimization techniques readily available in software packages like Caret.

From a theoretical standpoint, the findings provoke a reevaluation of past conclusions drawn from defect prediction models using default settings, particularly those employing popular algorithms like random forests. There is a clear indication that defect models require tailored optimization strategies based on the classification technique and dataset characteristics.

In the context of future advancements, this paper opens doors to exploring more sophisticated optimization algorithms and potentially integrating machine learning-driven parameter optimization strategies. Such developments could further enhance the adaptability and performance of software defect predictions across varying contexts.

In conclusion, the paper makes a significant contribution by shedding light on the necessity of parameter optimization in defect prediction models. It underscores that while some commonly used techniques like random forests may not benefit extensively, others can see profound improvements, ultimately enriching the research landscape with insights actionable in real-world software engineering.