Mingliang Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mingliang Ma has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Lin Chen
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
4 shared publications - Yanhui Li
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
4 shared publications - Yuming Zhou
Nanjing University, State Key Laboratory for Novel Software Technology, Nanjing, China
3 shared publications - Wenjie Xu
State Key Laboratory for Novel Software Technology, Nanjing University, China
2 shared publications - Baowen Xu
Nanjing University, Nanjing, China
1 shared publication Jun Wang
1 shared publication
Latest Publications
- 25 March 2026
- Published by Elsevier in Science of Computer Programming
- Vol. 253
MutDBD: Mutation-based training set diagnosis for backdoor defense in deep neural networks
- 25 March 2026
- Published by Elsevier in Science of Computer Programming
- Vol. 253
MutDBD: Mutation-based training set diagnosis for backdoor defense in deep neural networks
- Open Access
- 20 January 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Software Engineering and Methodology
- Vol. 35 (2) , 1-36
Less Is More: Feature Engineering for Fairness and Performance of Machine Learning Software
Machine Learning (ML) software employs statistical algorithms to perform high-stake tasks in our daily lives, whose results are usually discriminatory due to protected features (e.g., gender), i.e., one part (called privileged, e.g., male) may be more likely to obtain beneficial decisions than the other part (called unprivileged, e.g., female). In alleviating the unfairness, developers have obtained widely held beliefs about the tradeoff between performance and fairness for ML software. Surprisingly, recent research on feature engineering suggests that enlarging the feature set is the perfect way to kill two birds with one stone, i.e., achieving both higher performance and fairness. However, the experiments used in the prior study did not remove the effect of protected features, which have been suggested to be excluded in both industrial applications and academic studies. As a result, the study did not fully explore the tradeoff between performance and fairness. In this article, we first conduct an empirical study to replicate this prior study after excluding the protected features and observe that there is still a tradeoff between performance and fairness with enlarging the features, i.e., more features are not perfect, which would lead to higher performance and lower fairness. Due to more features causing more collection and pre-processing budgets, we aim to search for an effective alternative. Inspired by the “less is more” principle, we propose a novel feature ranking method, Hybrid-importance and Early-validation based Feature Ranking (HEFR) , to find an efficient subset to replace the full feature set with comparable performance and fairness. Our method, HEFR, employs hybrid feature importances to combine performance and fairness and conducts early validation to check the effectiveness of hybrid importances. We conduct experiments on seven datasets and three classifiers to evaluate our method with five baselines. The results have shown that (a) HEFR is efficient for ML software feature engineering: applying HEFR to choose about 10% of features would construct ML software with better or comparable performance and fairness, and (b) HEFR is actionable with small dataset sizes: applying HEFR with only 10% data size would still help choose the proper feature subset.
- 20 January 2026
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Software Engineering and Methodology
- Vol. 35 (2) , 1-36
Open AccessLess Is More: Feature Engineering for Fairness and Performance of Machine Learning Software
Machine Learning (ML) software employs statistical algorithms to perform high-stake tasks in our daily lives, whose results are usually discriminatory due to protected features (e.g., gender), i.e., one part (called privileged, e.g., male) may be more likely to obtain beneficial decisions than the other part (called unprivileged, e.g., female). In alleviating the unfairness, developers have obtained widely held beliefs about the tradeoff between performance and fairness for ML software. Surprisingly, recent research on feature engineering suggests that enlarging the feature set is the perfect way to kill two birds with one stone, i.e., achieving both higher performance and fairness. However, the experiments used in the prior study did not remove the effect of protected features, which have been suggested to be excluded in both industrial applications and academic studies. As a result, the study did not fully explore the tradeoff between performance and fairness. In this article, we first conduct an empirical study to replicate this prior study after excluding the protected features and observe that there is still a tradeoff between performance and fairness with enlarging the features, i.e., more features are not perfect, which would lead to higher performance and lower fairness. Due to more features causing more collection and pre-processing budgets, we aim to search for an effective alternative. Inspired by the “less is more” principle, we propose a novel feature ranking method, Hybrid-importance and Early-validation based Feature Ranking (HEFR) , to find an efficient subset to replace the full feature set with comparable performance and fairness. Our method, HEFR, employs hybrid feature importances to combine performance and fairness and conducts early validation to check the effectiveness of hybrid importances. We conduct experiments on seven datasets and three classifiers to evaluate our method with five baselines. The results have shown that (a) HEFR is efficient for ML software feature engineering: applying HEFR to choose about 10% of features would construct ML software with better or comparable performance and fairness, and (b) HEFR is actionable with small dataset sizes: applying HEFR with only 10% data size would still help choose the proper feature subset.
- Open Access
- 12 February 2025
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Software Engineering and Methodology
Weighted Suspiciousness and Balanced Aggregation to Boost Spectrum-based Fault Localization of Deep Learning Models
Deep learning (DL) models have proven to be highly successful and are now essential to our everyday routines. However, DL models, like traditional software, inevitably contain bugs that affect their performance in real-world scenarios. Effective software engineering techniques are necessary to ensure their dependability. In recent years, fault localization methods for DL models have gained significant attention as a valuable tool for improving the reliability of DL models. Owing to the data-driven programming paradigm, traditional fault localization techniques are challenging to apply directly to DL programs. Previous studies have shown that neuron errors within models can lead to abnormal behavior, and they fix the DL model errors from the perspective of neurons. Nonetheless, there remains a significant gap between the DL program statement and model errors. To tackle this problem, this paper proposes a novel fault localization method for DL models, named wei G hted s U sp I ciousness an D balanc E d agg R egation ( \(\mathsf{GUIDER}\) ) that revisits the idea and challenge of spectrum-based fault localization in the context of DL models. For pre-trained DL models, \(\mathsf{GUIDER}\) utilizes neuron coverage information and test case confidence to compute weighted neuron suspiciousness values and employs balanced aggregation methods to elevate these values from the neuron level to the layer level, which establishes a bridge between the DL model and the DL program, facilitating the developers’ debugging process. We evaluate \(\mathsf{GUIDER}\) using 161 real model bugs collected from StackOverflow and five state-of-the-art fault localization methods for DL models as baselines. The results indicate that (a) our method successfully localizes 67% of the model bugs by ranking the buggy layer to the first place (i.e., top- \(1\) ), significantly outperforming all five baselines, and (b) our method maintains an acceptable time overhead compared with all baseline methods.- 12 February 2025
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Software Engineering and Methodology
Open AccessWeighted Suspiciousness and Balanced Aggregation to Boost Spectrum-based Fault Localization of Deep Learning Models
Deep learning (DL) models have proven to be highly successful and are now essential to our everyday routines. However, DL models, like traditional software, inevitably contain bugs that affect their performance in real-world scenarios. Effective software engineering techniques are necessary to ensure their dependability. In recent years, fault localization methods for DL models have gained significant attention as a valuable tool for improving the reliability of DL models. Owing to the data-driven programming paradigm, traditional fault localization techniques are challenging to apply directly to DL programs. Previous studies have shown that neuron errors within models can lead to abnormal behavior, and they fix the DL model errors from the perspective of neurons. Nonetheless, there remains a significant gap between the DL program statement and model errors. To tackle this problem, this paper proposes a novel fault localization method for DL models, named wei G hted s U sp I ciousness an D balanc E d agg R egation ( \(\mathsf{GUIDER}\) ) that revisits the idea and challenge of spectrum-based fault localization in the context of DL models. For pre-trained DL models, \(\mathsf{GUIDER}\) utilizes neuron coverage information and test case confidence to compute weighted neuron suspiciousness values and employs balanced aggregation methods to elevate these values from the neuron level to the layer level, which establishes a bridge between the DL model and the DL program, facilitating the developers’ debugging process. We evaluate \(\mathsf{GUIDER}\) using 161 real model bugs collected from StackOverflow and five state-of-the-art fault localization methods for DL models as baselines. The results indicate that (a) our method successfully localizes 67% of the model bugs by ranking the buggy layer to the first place (i.e., top- \(1\) ), significantly outperforming all five baselines, and (b) our method maintains an acceptable time overhead compared with all baseline methods.