Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 16:00 - 16:20 at Cortez 1 - Prediction Chair(s): Xin Xia

Open source software licenses regulate the circum- stances under which software can be redistributed, reused and modified. Ensuring license compatibility and preventing license restriction conflicts among source code during software changes is the key to protect their commercial use. However, selecting ap- propriate licenses for software changes requires lots of experience and manual effort to examine, assimilate and compare various licenses as well as understand their relationships with software changes. Worse still, there is no state-of-the-art methodology to provide this capability. Motivated by this observation, we propose in this paper Automatic License Prediction (ALP), a novel learning-based method and tool for predicting licenses as software changes. An extensive evaluation of ALP on predicting licenses in 700 open source projects demonstrate its effectiveness: ALP can achieve not only a high overall prediction accuracy (i.e., 92.5% in micro F1-score) but also high accuracies across all license types.

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