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Further Causal Search Analyses With UCC's Effort Estimation Data

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Author
Hira, Anandi
Boehm, Barry
Stoddard, Robert
Konrad, Michael
Date
2018-04-30
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Abstract
Correlation does not imply causation. Though this is a well-known fact, most analyses depend on correlation as proof of relationships that are often treated as causal. Causal search, also referred to as causal discovery, involves the application of statistical methods to identify causal relationships using conditional independences (and/or other statistical relationships) within data. Though software cost estimation models use both domain knowledge and statistics, to date, there has yet to be a published report describing the evaluation of a software dataset using causal search. In a previous paper, the authors ran a PC causal search algorithm on Unified Code Count's (UCC's)1 dataset of maintenance tasks and compared them to correlation test results. This paper builds on the previous paper to introduce causal discovery to software engineering research by exploring additional causal search algorithms (PC-Stable, fast greedy equivalent search [FGES], and fast adjacency skewness [FASK]) and comparing their results to the traditional multi-step regression analysis.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
URI
http://hdl.handle.net/10945/58781
NPS Report Number
SYM-AM-18-097
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  • Acquisition Research Symposium

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