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 NovDisplayed time zone: Tijuana, Baja California change
16:00 - 17:40 | PredictionResearch Papers / Journal First Presentations at Cortez 1 Chair(s): Xin Xia Monash University | ||
16:00 20mTalk | Predicting Licenses for Changed Source Code Research Papers Xiaoyu Liu Department of Computer Science and Engineering, Southern Methodist University, Liguo Huang Dept. of Computer Science, Southern Methodist University, Dallas, TX, 75205, Jidong Ge State Key Laboratory for Novel Software and Technology, Nanjing University, Vincent Ng Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688 | ||
16:20 20mTalk | Empirical evaluation of the impact of class overlap on software defect prediction Research Papers Lina Gong China University of Mining and Technology, Shujuan Jiang China University of Mining and Technology, Rongcun Wang China University of Mining and Technology, Li Jiang China University of Mining and Technology | ||
16:40 20mTalk | Combining Program Analysis and Statistical Language Model for Code Statement Completion Research Papers Son Nguyen The University of Texas at Dallas, Tien N. Nguyen University of Texas at Dallas, Yi Li New Jersey Institute of Technology, USA, Shaohua Wang New Jersey Institute of Technology, USA | ||
17:00 20mTalk | Balancing the trade-off between accuracy and interpretability in software defect prediction Journal First Presentations Toshiki Mori Corporate Software Engineering & Technology Center, Toshiba Corporation, Naoshi Uchihira School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST) Link to publication File Attached | ||
17:20 20mTalk | Fine-grained just-in-time defect prediction Journal First Presentations Luca Pascarella Delft University of Technology, Fabio Palomba Department of Informatics, University of Zurich, Alberto Bacchelli University of Zurich Link to publication | ||