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ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States

ASE 2019 introduces a Late Breaking Results (LBR) track. This track aims to promote discussion of the most recent, late-breaking advances towards cutting-edge or emerging research results with the broad scientific community attending the conference. By submitting to this track, participants can get early feedback before having to evaluate or even build a tool, idea, algorithm, or experiment.

Accepted Papers

Title
A Process Mining based Approach to Improving Defect Detection of SysML Models. Pre-print
API Misuse Correction: A Statistical Approach Pre-print
An Empirical Study on the Characteristics of Question-Answering Process on Developer Forums Pre-print
Data Sanity Check for Deep Learning Systems via Learnt Assertions Pre-print
Detecting Deep Neural Network Defects with Data Flow Analysis Pre-print
K-CONFIG: Using Failing Test Cases to Generate Test Cases in GCC Compilers Pre-print
LVMapper: A Large-variance Clone Detector Using Sequencing Alignment Approach Pre-print
Learning test traces Pre-print
On building an automated responding system for app reviews: What are the characteristics of reviews and their responses? Pre-print
Open-Source Projects and their Collaborative Development Workflows Pre-print
Recommendation of Exception Handling Code in Mobile App Development Pre-print
Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations Pre-print
Should We Add Repair Time to an Unfixed Bug? An Exploratory Study of Automated Program Repair on 2980 Small-Scale Programs Pre-print
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization Pre-print
Testing Neural Programs Pre-print
The Dynamics of Software Composition Analysis Pre-print

Call for Papers

The topics of the LBR track are the same as the topics of the ASE 2019 research track.

A submission is expected to address a non-trivial problem by presenting a novel and sound method, but does not require preliminary results or a detailed evaluation in order to attract more recent work that is still in progress. Submissions which challenge the status-quo or have controversial ideas are encouraged. In addition, the LBR track provides a platform to seek comments and suggestions on ongoing work. We hope that the feedback from the LBR track will help research to mature to submissions to future top software engineering conferences. Please note that merely summarizing an existing paper does not qualify for this track.

Accepted LBR papers will be presented as part of an interactive poster session, where presenters and participants are encouraged to discuss lively the presented work.

PROCEEDINGS

LBR papers will not appear in the official ASE proceedings. Instead, authors of accepted LBR papers are encouraged to upload their papers on ArXiv.org. The ASE 2019 website will link to these papers and/or posters. The copyright is retained by the authors. We expect that authors can use the LBR papers as the basis for future publications as the LBR papers are not formally published.

SUBMISSION

Papers must be submitted electronically though EasyChair.

LBR papers must not exceed 2 pages plus up to 1 page that contain ONLY references.

The LBR track will use a double-blind review process.

All submissions must be in English, in PDF format and conform, at time of submission, to the IEEE Conference Proceedings Formatting Guidelines (title in 24pt font and full text in 10pt type, LaTeX users must use \documentclass[10pt,conference]{IEEEtran} without including the compsoc or compsocconf option).

Tracks

Wed 13 Nov

ase-2019-catering
15:20 - 16:00: Social - Break at Cortez Foyer/Kensington Terrace
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