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Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation
Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heur...
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| Published in: | IEEE transactions on software engineering 2008-07, Vol.34 (4), p.471-484 |
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| cites | cdi_FETCH-LOGICAL-c411t-f9078a42623a58de385df783da29bd51294af2deef388a5034bc5e96db428643 |
| container_end_page | 484 |
| container_issue | 4 |
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| container_title | IEEE transactions on software engineering |
| container_volume | 34 |
| creator | Keung, J.W. Kitchenham, B.A. Jeffery, D.R. |
| description | Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset. The method is demonstrated using the Desharnais dataset and two random datasets, showing (1) the use of Mantel's correlation to identify whether analogy is appropriate, (2) a stepwise procedure for feature selection, as well as (3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X, thus, provides a sound statistical basis for analogy, removes the need for heuristic search and greatly improves its algorithmic performance. |
| doi_str_mv | 10.1109/TSE.2008.34 |
| format | article |
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Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset. The method is demonstrated using the Desharnais dataset and two random datasets, showing (1) the use of Mantel's correlation to identify whether analogy is appropriate, (2) a stepwise procedure for feature selection, as well as (3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X, thus, provides a sound statistical basis for analogy, removes the need for heuristic search and greatly improves its algorithmic performance.</description><subject>Algorithms</subject><subject>Analogies</subject><subject>Australia</subject><subject>Automation</subject><subject>Computer programs</subject><subject>Computer Society</subject><subject>Correlation</subject><subject>Cost estimates</subject><subject>Cost estimation</subject><subject>Costs</subject><subject>Data points</subject><subject>Datasets</subject><subject>Digital Object Identifier</subject><subject>Feature selection</subject><subject>Heuristic</subject><subject>Heuristic algorithms</subject><subject>Inference</subject><subject>Input variables</subject><subject>Linear regression</subject><subject>Management</subject><subject>Methods</subject><subject>R&D</subject><subject>Regression analysis</subject><subject>Research & development</subject><subject>Searching</subject><subject>Sensitivity 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and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on software engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Keung, J.W.</au><au>Kitchenham, B.A.</au><au>Jeffery, D.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation</atitle><jtitle>IEEE transactions on software engineering</jtitle><stitle>TSE</stitle><date>2008-07-01</date><risdate>2008</risdate><volume>34</volume><issue>4</issue><spage>471</spage><epage>484</epage><pages>471-484</pages><issn>0098-5589</issn><eissn>1939-3520</eissn><coden>IESEDJ</coden><abstract>Data-intensive analogy has been proposed as a means of software cost estimation as an alternative to other data intensive methods such as linear regression. Unfortunately, there are drawbacks to the method. There is no mechanism to assess its appropriateness for a specific dataset. In addition, heuristic algorithms are necessary to select the best set of variables and identify abnormal project cases. We introduce a solution to these problems based upon the use of the Mantel correlation randomization test called Analogy-X. We use the strength of correlation between the distance matrix of project features and the distance matrix of known effort values of the dataset. The method is demonstrated using the Desharnais dataset and two random datasets, showing (1) the use of Mantel's correlation to identify whether analogy is appropriate, (2) a stepwise procedure for feature selection, as well as (3) the use of a leverage statistic for sensitivity analysis that detects abnormal data points. Analogy-X, thus, provides a sound statistical basis for analogy, removes the need for heuristic search and greatly improves its algorithmic performance.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSE.2008.34</doi><tpages>14</tpages></addata></record> |
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| ispartof | IEEE transactions on software engineering, 2008-07, Vol.34 (4), p.471-484 |
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| source | ABI/INFORM Global; IEEE Electronic Library (IEL) Journals |
| subjects | Algorithms Analogies Australia Automation Computer programs Computer Society Correlation Cost estimates Cost estimation Costs Data points Datasets Digital Object Identifier Feature selection Heuristic Heuristic algorithms Inference Input variables Linear regression Management Methods R&D Regression analysis Research & development Searching Sensitivity analysis Software Software algorithms Software Engineering Statistical analysis Statistical inference Statistical methods Studies Systematic review Systems design Testing Variables |
| title | Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation |
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