Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 17:00 - 17:20 at Cortez 1 - Prediction Chair(s): Xin Xia

Context: Classification techniques of supervised machine learning have been successfully applied to various domains of practice. When building a predictive model, there are two important criteria: predictive accuracy and interpretability, which generally have a trade-off relationship. In particular, interpretability should be accorded greater emphasis in the domains where the incorporation of expert knowledge into a predictive model is required. Objective: The aim of this research is to propose a new classification model, called superposed naive Bayes (SNB), which transforms a naive Bayes ensemble into a simple naive Bayes model by linear approximation. Method: In order to evaluate the predictive accuracy and interpretability of the proposed method, we conducted a comparative study using well-known classification techniques such as rule-based learners, decision trees, regression models, support vector machines, neural networks, Bayesian learners, and ensemble learners, over 13 real-world public datasets. Results: A trade-off analysis between the accuracy and interpretability of different classification techniques was performed with a scatter plot comparing relative ranks of accuracy with those of interpretability. The experiment results show that the proposed method (SNB) can produce a balanced output that satisfies both accuracy and interpretability criteria. Conclusions: SNB offers a comprehensible predictive model based on a simple and transparent model structure, which can provide an effective way for balancing the trade-off between accuracy and interpretability.

Balancing the Trade-off between Accuracy and Interpretability in Software Defect Prediction (ASE2019_20191112a.pdf)927KiB

Wed 13 Nov

16:00 - 17:40: Papers - Prediction at Cortez 1
Chair(s): Xin XiaMonash University
ase-2019-papers16:00 - 16:20
Predicting Licenses for Changed Source Code
Xiaoyu LiuDepartment of Computer Science and Engineering, Southern Methodist University, Liguo HuangDept. of Computer Science, Southern Methodist University, Dallas, TX, 75205, Jidong GeState Key Laboratory for Novel Software and Technology, Nanjing University, Vincent NgHuman Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688
ase-2019-papers16:20 - 16:40
Empirical evaluation of the impact of class overlap on software defect prediction
Lina GongChina University of Mining and Technology, Shujuan JiangChina University of Mining and Technology, Rongcun WangChina University of Mining and Technology, Li JiangChina University of Mining and Technology
ase-2019-papers16:40 - 17:00
Combining Program Analysis and Statistical Language Model for Code Statement Completion
Son NguyenThe University of Texas at Dallas, Tien N. NguyenUniversity of Texas at Dallas, Yi LiNew Jersey Institute of Technology, USA, Shaohua WangNew Jersey Institute of Technology, USA
ase-2019-Journal-First-Presentations17:00 - 17:20
Balancing the trade-off between accuracy and interpretability in software defect prediction
Toshiki MoriCorporate Software Engineering & Technology Center, Toshiba Corporation, Naoshi UchihiraSchool of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST)
Link to publication File Attached
ase-2019-Journal-First-Presentations17:20 - 17:40
Fine-grained just-in-time defect prediction
Luca PascarellaDelft University of Technology, Fabio PalombaDepartment of Informatics, University of Zurich, Alberto BacchelliUniversity of Zurich
Link to publication