Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States

Modern software development relies heavily on Application Programming Interface (API) libraries. However, there are often certain constraints on using API elements in such libraries. Failing to follow such constraints (API misuse) could lead to serious programming errors. Many approaches have been proposed to detect API misuses, but they still have low accuracy and cannot repair the detected misuses. In this paper, we propose SAM, a novel approach to detect and repair API misuses automatically. SAM uses statistical models to describe five factors involving in any API method call: related method calls, exceptions, pre-conditions, post-conditions, and values of arguments. These statistical models are trained from a large repository of high-quality production code. Then, given a piece of code, SAM verifies each of its method calls with the trained statistical models. If a factor has a sufficiently low probability, the corresponding call is considered as an API misuse. SAM performs an optimal search for editing operations to apply on the code until it has no API issue.

Wed 13 Nov

ase-2019-Late-Breaking-Results
15:20 - 16:00: Late Breaking Results - Poster Session: Late Breaking Results at Kensington Ballroom
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Recommendation of Exception Handling Code in Mobile App Development Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
LVMapper: A Large-variance Clone Detector Using Sequencing Alignment Approach
Ming Wu, Pengcheng WangUniversity of Science and Technology of China, Kangqi Yin, Haoyu Cheng, Yun XuUniversity of Science and Technology of China, Chanchal K. RoyUniversity of Saskatchewan
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
K-CONFIG: Using Failing Test Cases to Generate Test Cases in GCC Compilers Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
An Empirical Study on the Characteristics of Question-Answering Process on Developer Forums
Yi LiNanyang Technological University, Shaohua WangNew Jersey Institute of Technology, USA, Tien N. NguyenUniversity of Texas at Dallas, Son NguyenThe University of Texas at Dallas, Xinyue Ye, Yan Wang
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Testing Neural Programs
Md Rafiqul Islam RabinUniversity of Houston, Ke WangVisa Research, Mohammad Amin Alipour
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Data Sanity Check for Deep Learning Systems via Learnt Assertions
Haochuan LuFudan University, Huanlin Xu, Nana Liu, Yangfan ZhouFudan University, Xin Wang
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
Joymallya ChakrabortyNorth Carolina State University, Tianpei Xia, Fahmid M. Fahid, Tim MenziesNorth Carolina State University
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
API Misuse Correction: A Statistical Approach Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Should We Add Repair Time to an Unfixed Bug? An Exploratory Study of Automated Program Repair on 2980 Small-Scale Programs Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Learning test traces Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
The Dynamics of Software Composition Analysis Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
A Process Mining based Approach to Improving Defect Detection of SysML Models.
Mounifah Alenazi, Nan NiuUniversity of Cincinnati, Juha SavolainenDanfoss
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Open-Source Projects and their Collaborative Development Workflows
panuchart bunyakiatikasetsart university, Usa Sammapunkasetsart university
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
Detecting Deep Neural Network Defects with Data Flow Analysis
Jiazhen Gu, Huanlin Xu, Yangfan ZhouFudan University, Xin Wang, Hui Xu, Michael LyuThe Chinese University of Hong Kong
Pre-print
ase-2019-Late-Breaking-Results15:20 - 16:00
Poster
On building an automated responding system for app reviews: What are the characteristics of reviews and their responses? Pre-print