4.6 Article

Towards Automatically Localizing Function Errors in Mobile Apps With User Reviews

Related references

Note: Only part of the references are listed.
Article Computer Science, Software Engineering

ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking

Shangqing Liu et al.

Summary: Commit messages are records of code changes and help with program comprehension, but developers are often unwilling to write them. Automating the message generation process is necessary. By incorporating abstract syntax tree and combining retrieved and generated messages, accuracy can be improved.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2022)

Review Computer Science, Software Engineering

User Review-Based Change File Localization for Mobile Applications

Yu Zhou et al.

Summary: In current mobile app development, the importance of user feedback is increasing, and the RISING method categorizes, clusters, and links user reviews to locate potential software files for changes, thus enhancing the efficiency of continuous integration of user feedback.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2021)

Article Computer Science, Software Engineering

Where2Change: Change Request Localization for App Reviews

Tao Zhang et al.

Summary: A new approach is proposed to improve the accuracy of source code change localization by associating user feedback from reviews with issue reports. Experimental results show that the new method achieves higher accuracy and recall rates than CHANGEADVISOR and outperforms other information retrieval technologies.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2021)

Proceedings Paper Computer Science, Software Engineering

An NLP-based Tool for Software Artifacts Analysis

Andrea Di Sorbo et al.

Summary: Software developers rely on various repositories and channels to exchange information, with researchers leveraging semi-structured and unstructured software artifacts to build recommender systems. Natural Language parsing techniques automate the identification of relevant information, but manual pattern identification is still required. NEON is an NL parsing-based tool that automates rule mining, reducing manual effort for software artifact analysis.

2021 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2021) (2021)

Proceedings Paper Computer Science, Information Systems

PrivacyFlash Pro: Automating Privacy Policy Generation for Mobile Apps

Sebastian Zimmeck et al.

Summary: This study shows that privacy policies generated with popular policy generators may not accurately reflect apps' privacy practices. By combining questionnaire-based methods with code analysis, PrivacyFlash Pro was developed to identify apps' privacy practices reliably. The tool was tested with 40 iOS app developers, showing promising results in identifying privacy practices and usability.

28TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2021) (2021)

Article Computer Science, Software Engineering

code2vec: Learning Distributed Representations of Code

Uri Alon et al.

PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL (2019)

Article Computer Science, Software Engineering

Enhancing the Description-to-Behavior Fidelity in Android Apps with Privacy Policy

Le Yu et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2018)

Article Computer Science, Theory & Methods

Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

Ming Fan et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2018)

Proceedings Paper Computer Science, Theory & Methods

Localizing Function Errors in Mobile Apps with User Reviews

Le Yu et al.

2018 48TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN) (2018)

Article Computer Science, Theory & Methods

DAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis

Ming Fan et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2017)

Article Computer Science, Theory & Methods

Toward Automatically Generating Privacy Policy for Android Apps

Le Yu et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2017)

Article Computer Science, Software Engineering

A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software

Alireza Sadeghi et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2017)

Article Computer Science, Software Engineering

A Survey of App Store Analysis for Software Engineering

William Martin et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2017)

Review Computer Science, Software Engineering

A systematic literature review: Opinion mining studies from mobile app store user reviews

Necmiye Genc-Nayebi et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2017)

Proceedings Paper Computer Science, Software Engineering

Adaptive Unpacking of Android Apps

Lei Xue et al.

2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE) (2017)

Proceedings Paper Computer Science, Artificial Intelligence

HDLTex: Hierarchical Deep Learning for Text Classification

Kamran Kowsari et al.

2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (2017)

Proceedings Paper Computer Science, Software Engineering

Recommending and Localizing Change Requests for Mobile Apps based on User Reviews

Fabio Palomba et al.

2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE) (2017)

Proceedings Paper Computer Science, Software Engineering

Bug Report Enrichment with Application of Automated Fixer Recommendation

Tao Zhang et al.

2017 IEEE/ACM 25TH INTERNATIONAL CONFERENCE ON PROGRAM COMPREHENSION (ICPC) (2017)

Proceedings Paper Computer Science, Information Systems

Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis

Andrea Continella et al.

24TH ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2017) (2017)

Article Computer Science, Software Engineering

Automatic Source Code Summarization of Context for Java Methods

Paul W. McBurney et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2016)

Article Computer Science, Information Systems

On the automatic classification of app reviews

Walid Maalej et al.

REQUIREMENTS ENGINEERING (2016)

Proceedings Paper Computer Science, Software Engineering

Revisiting the Description-to-Behavior Fidelity in Android applications

Le Yu et al.

2016 IEEE 23RD INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER), VOL 1 (2016)

Article Computer Science, Hardware & Architecture

VULHUNTER: TOWARD DISCOVERING VULNERABILITIES IN ANDROID APPLICATIONS

Chenxiong Qian et al.

IEEE MICRO (2015)

Article Computer Science, Software Engineering

What Do Mobile App Users Complain About?

Hammad Khalid et al.

IEEE SOFTWARE (2015)

Proceedings Paper Computer Science, Theory & Methods

Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval

Dae Hoon Park et al.

SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (2015)

Proceedings Paper Computer Science, Hardware & Architecture

A Recommender System of Buggy App Checkers for App Store Moderators

Maria Gomez et al.

2ND ACM INTERNATIONAL CONFERENCE ON MOBILE SOFTWARE ENGINEERING AND SYSTEMS MOBILESOFT 2015 (2015)

Proceedings Paper Computer Science, Information Systems

DexHunter: Toward Extracting Hidden Code from Packed Android Applications

Yueqian Zhang et al.

COMPUTER SECURITY - ESORICS 2015, PT II (2015)

Proceedings Paper Computer Science, Software Engineering

What parts of your apps are loved by users?

Xiaodong Gu et al.

2015 30TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE) (2015)

Proceedings Paper Computer Science, Information Systems

EdgeMiner: Automatically Detecting Implicit Control Flow Transitions through the Android Framework

Yinzhi Cao et al.

22ND ANNUAL NETWORK AND DISTRIBUTED SYSTEM SECURITY SYMPOSIUM (NDSS 2015) (2015)

Article Computer Science, Software Engineering

FlowDroid: Precise Context, Flow, Field, Object-sensitive and Lifecycle-aware Taint Analysis for Android Apps

Steven Arzt et al.

ACM SIGPLAN NOTICES (2014)

Proceedings Paper Computer Science, Software Engineering

AR-Miner: Mining Informative Reviews for Developers from Mobile App Marketplace

Ning Chen et al.

36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2014) (2014)

Article Ecology

A working guide to boosted regression trees

J. Elith et al.

JOURNAL OF ANIMAL ECOLOGY (2008)

Article Computer Science, Software Engineering

SNIAFL: Towards a static noninteractive approach to feature location

Wei Zhao et al.

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY (2006)