@Conference{DBLP:conf/wcre/KuangNHRLEM17,
  author    = {Hongyu Kuang and Jia Nie and Hao Hu and Patrick Rempel and Jian Lu and Alexander Egyed and Patrick Mäder},
  title     = {Analyzing closeness of code dependencies for improving IR-based Traceability Recovery},
  booktitle = {Proceedings of 24th International Conference on Software Analysis, Evolution and Reengineering (SANER 2017), Klagenfurt, Austria},
  year      = {2017},
  pages     = {68--78},
  abstract  = {Information Retrieval (IR) identifies traces based on textual similarities
	among software artifacts. However, the vocabulary mismatch problem
	between different artifacts hinders the performance of IR-based approaches.
	A growing body of work addresses this issue by combining IR techniques
	with code dependency analysis such as method calls. However, so far
	combined approaches considered each code dependency as equally helpful
	for traceability recovery, not taking full advantage of the code
	dependency analysis. In this paper, we combine IR techniques with
	closeness analysis on code dependencies to improve IR-based traceability
	recovery. Specifically, we quantify and utilize the “closeness” for
	each call and data dependency between two classes to improve rankings
	of traceability candidate lists. An empirical evaluation based on
	three real-world systems suggests that our approach outperforms baseline
	approaches.},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/wcre/KuangNHRLEM17},
  crossref  = {DBLP:conf/wcre/2017},
  doi       = {10.1109/SANER.2017.7884610},
  file      = {:Conferences\\SANER 2017 - Analyzing Closeness of Code Dependencies for Improving IR-Based Traceability Recovery\\Analyzing Closeness of Code Dependencies for Improving IR-based Traceability Recovery-preprint.pdf:PDF},
  keywords  = {FWF P23115},
  timestamp = {Fri, 26 May 2017 00:48:18 +0200},
  url       = {https://doi.org/10.1109/SANER.2017.7884610},
}