Towards a semantic knowledge base on threats to validity and control actions in controlled experiments. Biffl, S., Kalinowski, M., Ekaputra, F. J., Anderlin-Neto, A., Conte, T., & Winkler, D. In 2014 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM '14, Torino, Italy, September 18-19, 2014, pages 49:1-49:4, 2014.
Author version doi abstract bibtex [Context] Experiment planners need to be aware of relevant Threats to Validity (TTVs), so they can devise effective control actions or accept the risk. [Objective] The aim of this paper is to introduce a TTV knowledge base (KB) that supports experiment planners in identifying relevant TTVs in their research context and actions to control these TTVs. [Method] We identified requirements, designed and populated a TTV KB with data extracted during a systematic review: 63 TTVs and 149 control actions from 206 peer-reviewed published software engineering experiments. We conducted an initial proof of concept on the feasibility of using the TTV KB and analyzed its content. [Results] The proof of concept and content analysis provided indications that experiment planners can benefit from an extensible TTV KB for identifying relevant TTVs and control actions in their specific context. [Conclusions] The TTV KB should be further evaluated and evolved in a variety of software engineering contexts. Copyright 2014 ACM.
@inproceedings{BifflKENCW14,
author = {Stefan Biffl and
Marcos Kalinowski and
Fajar J. Ekaputra and
Amadeu Anderlin-Neto and
Tayana Conte and
Dietmar Winkler},
title = {Towards a semantic knowledge base on threats to validity and control
actions in controlled experiments},
abstract = {[Context] Experiment planners need to be aware of relevant Threats to Validity (TTVs), so they can devise effective control actions or accept the risk. [Objective] The aim of this paper is to introduce a TTV knowledge base (KB) that supports experiment planners in identifying relevant TTVs in their research context and actions to control these TTVs. [Method] We identified requirements, designed and populated a TTV KB with data extracted during a systematic review: 63 TTVs and 149 control actions from 206 peer-reviewed published software engineering experiments. We conducted an initial proof of concept on the feasibility of using the TTV KB and analyzed its content. [Results] The proof of concept and content analysis provided indications that experiment planners can benefit from an extensible TTV KB for identifying relevant TTVs and control actions in their specific context. [Conclusions] The TTV KB should be further evaluated and evolved in a variety of software engineering contexts. Copyright 2014 ACM.},
booktitle = {2014 {ACM-IEEE} International Symposium on Empirical Software Engineering
and Measurement, {ESEM} '14, Torino, Italy, September 18-19, 2014},
pages = {49:1-49:4},
year = {2014},
urlAuthor_version = {http://www.inf.puc-rio.br/~kalinowski/publications/BifflKENCW14.pdf},
doi = {10.1145/2652524.2652568},
}
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