Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Tue 12 Nov 2019 16:40 - 17:00 at Cortez 2&3 - Code and Artifact Analysis Chair(s): Sarah Nadi

Developers often reuse code snippets from online forums, such as Stack Overflow, GitHub Gists to learn API usages of software frameworks or libraries. Those code snippets often have ambiguous undeclared external references. This makes it difficult to learn and use those APIs correctly. Reusing those code snippets to solve development tasks also requires resolving external references of those APIs. However, manually resolving fully qualified names (FQN) of API elements is a non-trivial task. In this paper, we propose a novel context-sensitive technique, COSTER, to resolve FQNs of API elements in those code snippets. The technique collects locally specific source code elements as well as globally related tokens as the context of FQNs, calculate association score, and build an occurrence likelihood dictionary. While inferring an API element, it collects the code context and ranks candidate FQNs from the dictionary by considering the association score of the tokens in the context, similarity between the context, and similarity between the API element. Evaluation with code examples collected from GitHub and Stack Overflow posts shows that our proposed technique improves precision and recall by 3-18% compared to existing state-of-the-art techniques. The proposed technique significantly reduces the training time compared to the StatType, a state-of-the-art technique, without sacrificing accuracy. Extensive analyses on results establish the facts of the robustness of the proposed technique.

Tue 12 Nov

16:00 - 17:40: Papers - Code and Artifact Analysis at Cortez 2&3
Chair(s): Sarah NadiUniversity of Alberta
ase-2019-papers16:00 - 16:20
Emotions Extracted from Text vs. True Emotions –An Empirical Evaluation in SE Context
Yi WangShenzhen University
ase-2019-Journal-First-Presentations16:20 - 16:40
Collaborative feature location in models through automatic query expansion
Francisca PérezSVIT Research GroupUniversidad San Jorge, Jaime FontSan Jorge University, Spain, Lorena ArcegaSan Jorge University, Carlos CetinaSan Jorge University, Spain
Link to publication
ase-2019-papers16:40 - 17:00
Learning from Examples to Find Fully Qualified Names of API Elements in Code Snippets
C M Khaled SaifullahDepartment of Computer Science, University of Saskatchewan, Muhammad AsaduzzamanPostdoctoral Research Fellow, Software Analysis and Intelligence Lab, Queen's University, Canada, Chanchal K. RoyUniversity of Saskatchewan
ase-2019-papers17:00 - 17:20
Inferring Program Transformations From Singular Examples via Big Code
Jiajun JiangPeking University, Luyao RenPeking University, Yingfei XiongPeking University, Lingming ZhangThe University of Texas at Dallas
Link to publication Pre-print
ase-2019-Journal-First-Presentations17:20 - 17:40
Extracting and studying the Logging-Code-Issue-Introducing changes in Java-based large-scale open source software systems
Boyuan ChenYork University, Zhen Ming (Jack) JiangYork University
Link to publication