輪講資料

予定†

MSR2015†

2015年6月9日†

Code Ownership and Software Quality: A Replication Study†

Mining Component Repositories for Installability Issues†

Co-evolution of Infrastructure and Source Code - An Empirical Study†

Mining Energy-Aware Commits†

Why Power Laws? An Explanation from Fine-Grained Code Changes†

An Empirical Study of the Copy and Paste Behavior during Development†

2015年6月23日†

The Uniqueness of Changes: Characteristics and Applications†

著者:Baishakhi Ray, Meiyappan Nagappan, Christian Bird, Nachiappan Nagappan, Thomas Zimmermann

6月23日†

Using Developer-Interaction Trails to Triage Change Requests†

Mining Android App Usages for Generating Actionable GUI-Based Execution Scenarios†

The App Sampling Problem for App Store Mining†

Unveiling Exception Handling Bug Hazards in Android Based on GitHub and Google Code Issues†

DataPaper†

2015年6月30日†

Extracting Facts from Performance Tuning History of Scientific Applications for Predicting Effective Optimization Patterns†

Sameness: An Experiment in Code Search†

Characteristics of Useful Code Reviews: An Empirical Study at Microsoft†

Will They Like This? Evaluating Code Contributions with Language Models†

Investigating Code Review Practices in Defective Files: An Empirical Study of the Qt System†

Partitioning Composite Code Changes to Facilitate Code Review†

Lessons Learned from Building and Deploying a Code Review Analytics Platform†

ICSE2015†

2015年6月30日: ICSE Tech.Res.†

W2.3 Mobile Applications†

2015年7月7日†

1. Code Review (that Passed Peer Review)†

1.5 Lessons Learned from Building and Deploying a Code Review Analytics Platform(松本)

2. Ecosystems, APIs, and Architecture†

2.1 Ecosystems in GitHub and a Method for Ecosystem Identification using Reference Coupling(山谷)

2.2 A historical analysis of Debian package incompatibilities(久木田)

2.3 Recommending Posts Concerning API Issues in Developer Q&A Sites(前田)

4. Scary stuff: Bugs, Risks, and Vulnerabilities†

4.1 Are These Bugs Really 'Normal'? (松野)

4.3 Characterization and prediction of issue-related risks in software projects (優太)

5. Computer Musicians Bullied for Using Gists†

5.2 Are Bullies more Productive? Empirical Study of Affectiveness vs. Issue Fixing Time

6. Licenses, Deep Learning, and Process Mining†

6.1 A Method to Detect License Inconsistencies in Large-Scale Open Source Projects

6.2 Toward Deep Learning Software Repositories(大典)

2015年7月14日†

2. Ecosystems, APIs, and Architecture†

2.4 An Empirical Study of Architectural Change in Open-Source Software Systems(前田)

2.5 A Study on the Role of Software Architecture in the Evolution and Quality of Software(山谷)

4. Scary stuff: Bugs, Risks, and Vulnerabilities†

4.2 Do Bugs Foreshadow Vulnerabilities? A Study of the Chromium Project (high impact班)

5. Computer Musicians Bullied for Using Gists†

5.3 What is the Gist? Understanding the Use of Public Gists on GitHub(久木田)

6. Licenses, Deep Learning, and Process Mining†

6.3 Identifying Software Process Management Challenges: Survey of Practitioners in a Large Global IT Company(東)

Perspectives on Software Engineering†

空白†

ICSE2015

W2.1 (東)

W2.2 (吉行)

W2.3 (ICSE勉強会)

W3.1(大典)

W3.2 (大典)

W4.2(吉行)

W4.3(前田)

T2.1 (大典)

T2.2 (山谷)

T2.3(久木田)

T3.1(大典)

T3.2 (優太)

T3.3(吉行)

T4.1(前田)

T4.2(片桐)

F1.2(山谷・片桐)

F2.2(優太・松本)

F2.3(松本)

F3.1(前田)

F3.2(松野)

F3.3(吉行)


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