Mitigating the Negative Impact of Defect Overlooking on Online Learning-based Software Defect Prediction Models

Khái niệm cốt lõi

Online learning-based software defect prediction models can be negatively impacted by defect overlooking during software testing, and a new method is proposed to mitigate this issue.

Tóm tắt

This paper focuses on the problem of software defect prediction models built using online learning, which can be affected by the overlooking of defects during software testing. When a module is predicted as "non-defective", fewer test cases are allocated for that module, leading to potential overlooking of defects. This overlooking distorts the learning data utilized by online learning, negatively impacting the prediction accuracy.

To address this issue, the authors propose two methods:

1. Fixed prediction method: This method forcibly turns the prediction as "defective" during the initial stage of online learning to suppress the influence of Type 1 overlooking (overlooking due to fewer test cases on negatively predicted modules).

2. Proposed method: This method builds on the fixed prediction method, but discontinues the fixed prediction when the rate of Type 1 overlooking is low, to avoid the potential degradation of precision caused by the fixed prediction.

The authors conducted experiments using three datasets and artificially manipulated the probability of overlooking. The results showed that:

• Without any mitigation method, the recall of the prediction models was degraded by over 10% when the probability of overlooking was 60% or greater.
• The fixed prediction method significantly improved the recall, but degraded the precision by over 5% on two out of three datasets, regardless of the overlooking probability.
• The proposed method avoided the negative impacts on AUC and F1 score when the overlooking probability was 80% or greater, while maintaining the steady improvement in recall compared to the existing approach.

The proposed method can effectively mitigate the negative impact of defect overlooking on online learning-based software defect prediction models, ensuring high accuracy and recall even when testing resources are drastically reduced for modules predicted as defective.

Thống kê

When the probability of Type 1 overlooking was 60% or greater, the recall of the prediction models built using online learning without any mitigation method was degraded by over 10%.
The fixed prediction method significantly improved the recall, but degraded the precision by over 5% on two out of three datasets, regardless of the overlooking probability.
The proposed method avoided the negative impacts on AUC and F1 score when the overlooking probability was 80% or greater.

Trích dẫn

"When the probability of Type 1 overlooking was 60% or greater, the recall of the prediction models built using online learning without any mitigation method was degraded by over 10%."
"The fixed prediction method significantly improved the recall, but degraded the precision by over 5% on two out of three datasets, regardless of the overlooking probability."
"The proposed method avoided the negative impacts on AUC and F1 score when the overlooking probability was 80% or greater."

Thông tin chi tiết chính được chắt lọc từ

Building Defect Prediction Models by Online Learning Considering Defect Overlooking

by Nikolay Fedo... lúc arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11033.pdf

Yêu cầu sâu hơn

How can the proposed method be further improved to maintain the benefits of the fixed prediction approach while minimizing the potential degradation of precision?

The proposed method can be enhanced by incorporating adaptive mechanisms that adjust the fixed prediction strategy based on the evolving performance of the prediction model. One approach could involve dynamically monitoring the precision levels during the online learning process. If the precision starts to degrade beyond a certain threshold, the fixed prediction method could be temporarily suspended to allow the model to adapt to the changing data patterns. This adaptive approach would ensure that the benefits of fixed prediction are maintained while preventing excessive degradation of precision.

What other factors, besides defect overlooking, could impact the performance of online learning-based software defect prediction models, and how can they be addressed?

Several factors can influence the performance of online learning-based defect prediction models. One significant factor is the quality and relevance of the features used for prediction. If the features are not representative of the underlying patterns of defects, the model's accuracy will be compromised. Feature selection techniques, such as correlation-based feature selection, can help address this issue by identifying the most relevant features for prediction.
Another factor is the imbalance in the dataset, where the number of defective and non-defective modules is skewed. This imbalance can lead to biased predictions and reduced model performance. Techniques like oversampling or undersampling can be employed to balance the dataset and improve the model's ability to predict defects accurately.
Furthermore, the presence of noisy or irrelevant data in the training set can also impact the model's performance. Data preprocessing techniques, such as outlier removal and data normalization, can help clean the dataset and improve the model's predictive capabilities.

How can the insights from this study be applied to other areas of software engineering where online learning techniques are employed, such as effort estimation or code quality assessment?

The insights from this study can be extrapolated to other areas of software engineering that utilize online learning techniques for prediction tasks. In the context of effort estimation, where predicting the time and resources required for software development tasks is crucial, the concept of fixed prediction to mitigate the impact of overlooking certain tasks can be applied. By identifying critical tasks early on and ensuring they receive adequate attention, the accuracy of effort estimation models can be improved.
Similarly, in code quality assessment, where predicting the likelihood of bugs or vulnerabilities in code is essential, the proposed method of handling defect overlooking can be valuable. By adjusting the prediction strategy based on the probability of overlooking defects, code quality assessment models can be fine-tuned to prioritize high-risk code segments for thorough evaluation, thereby enhancing the overall quality of the software product.