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.

Xiaoyu Liu

Department of Computer Science and Engineering, Southern Methodist University

Liguo Huang

Dept. of Computer Science, Southern Methodist University, Dallas, TX, 75205

United States

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

Software defect prediction (SDP) utilizes the learning models to detect the defective modules in project, and their performance depends on the quality of training data. The previous researches mainly focus on the quality problems of class imbalance and feature redundancy. However, training data often contain some instances that belong to different class but have similar values on features, and this leads to class overlap to affect the quality of training data. Our goal is to investigate the impact of class overlap on software defect prediction. At the same time, we propose an improved K-Means clustering cleaning approach (IKMCCA) to solve both the class overlap and class imbalance problems. Specifically, we check whether K-Means clustering cleaning approach (KMCCA) or neighborhood cleaning learning (NCL) or IKMCCA is feasible to improve defect detection performance for two cases (i) within-project defect prediction (WPDP) (ii) cross-project defect prediction (CPDP). To have an objective estimate of class overlap, we carry out our investigations on 28 open source projects, and compare the performance of state-of-the-art learning models for the above-mentioned cases by using IKMCCA or KMCCA or NCL VS. Without cleaning data. The experimental results make clear that learning models obtain significantly better performance in terms of balance, Recall and AUC for both WPDP and CPDP when the overlapping instances are removed. Moreover, it is better to consider both class overlap and class imbalance.

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

Automatic code completion helps improve developers’ productivity in their programming tasks. A program contains instructions expressed via code statements, which are considered as the basic units of program execution. In this paper, we introduce AutoSC, which combines program analysis and the principle of software naturalness to fill in partially completed statements. AutoSC benefits from the strengths of both directions, in which the completed code statement is both frequent and valid. AutoSC is first trained on a large code corpus to learn the templates of candidate statements. Then, it uses program analysis to validate and concretize the templates into syntactically and type-valid candidate statements. Finally, these candidates are ranked by using a language model trained on the lexical form of the source code in the code corpus. Our empirical evaluation shows that AutoSC achieves 38.9–41.3% top-1 and 48.2-50.1% top-5 accuracy in statement completion and outperforms the state-of-the-art approach from 9X–69X in top-1 accuracy.

Son Nguyen

The University of Texas at Dallas

United States

Tien N. Nguyen

University of Texas at Dallas

United States

Yi Li

New Jersey Institute of Technology, USA

Shaohua Wang

New Jersey Institute of Technology, USA

United States

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.

Link to Publication: https://link.springer.com/article/10.1007/s10664-018-9638-1

Toshiki Mori

Corporate Software Engineering & Technology Center, Toshiba Corporation

Japan

Naoshi Uchihira

School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST)

Japan

Defect prediction models focus on identifying defect-prone code elements, for example, to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e., at release time), a recent trend is represented by the so-called short-term defect prediction (i.e., at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects.

In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non-defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.

Link to Publication: https://www.sciencedirect.com/science/article/pii/S0164121218302656?via%3Dihub

Luca Pascarella

Delft University of Technology

Netherlands

Fabio Palomba

Department of Informatics, University of Zurich

Alberto Bacchelli

University of Zurich

Switzerland