مورد إلكتروني
Can We Predict Types of Code Changes? An Empirical Analysis
| العنوان: | Can We Predict Types of Code Changes? An Empirical Analysis |
|---|---|
| المؤلفون: | Giger, E. (author), Pinzger, M. (author), Gall, H.C. (author) |
| بيانات النشر: | Delft University of Technology, Software Engineering Research Group 2012-12-31 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Preprint of paper published in: 9th IEEE Working Conference on Mining Software Repositories (MSR), 2-3 June 2012; doi:10.1109/MSR.2012.6224284 There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category. Software Computer Technology Electrical Engineering, Mathematics and Computer Science |
| مصطلحات الفهرس: | software maintenance, machine learning, software quality, report, Text |
| URL: | Technical Report Series TUD-SERG-2012-018--1872-5392 |
| الإتاحة: | Open access content. Open access content (c) 2012 The Author(s) IEEE |
| ملاحظة: | English |
| أرقام أخرى: | NLTUD oai:tudelft.nl:uuid:1cf1480b-74bb-4f36-86f0-b1b15884206c 1008793180 |
| المصدر المساهم: | DELFT UNIV OF TECHNOL From OAIster®, provided by the OCLC Cooperative. |
| رقم الأكسشن: | edsoai.on1008793180 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |