Name
Abstract | ||
|---|---|---|
Feature location is the activity of identifying an initial location in the source code that implements special functionality in a software system. Existing techniques for feature location broadly fall into three categories, based on the type of information they use: text, static, and dynamic. The techniques based on dynamic may generate large amount of data and is difficult to utilize. This paper presents a method combing the text and static techniques. A feature location technique based on topic modeling is introduced, and the topic cohesion and coupling is computed by software dependency network to improve the effects of feature location. When the topic cohesion degree is low and topic coupling degree is high, software dependency network is used to find additional candidate program elements. This method is empirically evaluated through several experiments. Experimental results show that the topic modeling based feature location improves the effectiveness of feature location when compared with other techniques. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/APSEC.2012.116 | APSEC |
Keywords | Field | DocType |
feature location technique,topic cohesion,feature-based software product line,feature location,topic cohesion degree,text techniques,software dependency network,topic coupling,topic modeling,software feature location,topic coupling degree,static technique,product development,initial location,topic models,feature extraction,software feature location technique,software reusability,source code,text analysis,feature location effects improvement,static techniques,initial location identification,software system | Data mining,Feature-oriented domain analysis,Computer science,Feature (computer vision),Software system,Feature extraction,Feature model,Software,Software feature,Artificial intelligence,Topic model,Machine learning | Conference |
Volume | ISSN | ISBN |
1 | 1530-1362 | 978-1-4673-4930-7 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kunming Nie | 1 | 39 | 4.16 |
| Li Zhang | 2 | 141 | 20.37 |