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

Recent studies showed that the dialogs between app developers and app users on app stores are important to increase user satisfaction and app’s overall ratings. However, the large volume of reviews and the limitation of resources discourage app developers from engaging with customers through this channel. One solution to this problem is to develop an Automated Responding System for developers to respond to app reviews in a manner that is most similar to a human response. Toward designing such system, we have conducted an empirical study of the characteristics of mobile apps’ reviews and their human-written responses. We found that an app reviews can have multiple fragments at sentence level with different topics and intentions. Similarly, a response also can be divided into multiple fragments with unique intentions to answer certain parts of their review (e.g., complaints, requests, or information seeking). We have also identified several characteristics of review (rating, topics, intentions, quantitative text feature) that can be used to rank review by their priority of need for response. In addition, we identified the degree of re-usability of past responses is based on their context (single app, apps of the same category, and their common features). Last but not least, a responses can be reused in another review if some parts of it can be replaced by a placeholder that is either a named-entity or a hyperlink. Based on those findings, we discuss the implications of developing an Automated Responding System to help mobile apps’ developers write the responses for users reviews more effectively.

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