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| 2. |  | DUARTE, G. de S.; VEIGA, R. D.; MEIRA, S. R. Um modelo de regressao periodica na previsao de ferrugem alaranjada do cafeeiro. In: SIMPOSIO DE ESTATISTICA APLICADA A EXPERIMENTACAO AGRONOMICA, 4 & REUNIAO ANUAL DA REGIAO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 36., 1991. Goiania: EMBRAPA/CNPAF & UFG. Escola de Agronomia. Instituto de Ciencias Biologicas, 1992., v., p.50-54, 1992.| Biblioteca(s): Embrapa Cerrados. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Unidades Centrais. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
11/06/2010 |
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Data da última atualização: |
23/09/2019 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
PRADO, H. A. do; FERNEDA, E.; ANQUETIL, N.; TEIXEIRA, E. D'A. |
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Afiliação: |
HERCULES ANTONIO DO PRADO, SGE; EDILSON FERNEDA, Universidade Católica de Brasília; NICOLAS ANQUETIL, Universidade Católica de Brasília; ELIZABETH D'ARROCHELLA, Universidade Católica de Brasília. |
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Título: |
Counselor, a data mining based time estimation for software maintenance. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: In: VELÁSQUEZ, J. D.; RÍOS, S. A.; HOWLETT, R. J.; JAIN, L., C. (Ed.). Knowledge-Based and Intelligent Information and Engineering Systems 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part II. |
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Volume: |
5712 |
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Páginas: |
p. 364-371 |
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Série: |
(Lecture Notes in Computer Science - LNCS, 5712). |
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DOI: |
10.1007/978-3-642-04592-9_46 |
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Idioma: |
Inglês |
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Conteúdo: |
Measuring and estimating are fundamental activities for the success of any project. In the software maintenance realm the lack of maturity, or even a low level of interest in adopting effective maintenance techniques and related metrics, have been pointed out as an important cause for the high costs involved. In this paper data mining techniques are applied to provide a sound estimation for the time required to accomplish a maintenance task. Based on real world data regarding maintenance requests, some regression models are built to predict the time required for each maintenance. Data on the team skill and the maintenance characteristics are mapped into values that predict better time estimations in comparison to the one predicted by the human expert. A particular finding from this research is that the time prediction provided by a human expert works as an inductive bias that improves the overall prediction accuracy. |
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Palavras-Chave: |
Data mining; Informal reasoning; Software maintenance. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $a10.1007/978-3-642-04592-9_46$2DOI
100 1 $aPRADO, H. A. do
245 $aCounselor, a data mining based time estimation for software maintenance.$h[electronic resource]
260 $aIn: In: VELÁSQUEZ, J. D.; RÍOS, S. A.; HOWLETT, R. J.; JAIN, L., C. (Ed.). Knowledge-Based and Intelligent Information and Engineering Systems 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part II.$c2009
300 $ap. 364-371 5712
490 $a(Lecture Notes in Computer Science - LNCS, 5712).$v5712
520 $aMeasuring and estimating are fundamental activities for the success of any project. In the software maintenance realm the lack of maturity, or even a low level of interest in adopting effective maintenance techniques and related metrics, have been pointed out as an important cause for the high costs involved. In this paper data mining techniques are applied to provide a sound estimation for the time required to accomplish a maintenance task. Based on real world data regarding maintenance requests, some regression models are built to predict the time required for each maintenance. Data on the team skill and the maintenance characteristics are mapped into values that predict better time estimations in comparison to the one predicted by the human expert. A particular finding from this research is that the time prediction provided by a human expert works as an inductive bias that improves the overall prediction accuracy.
653 $aData mining
653 $aInformal reasoning
653 $aSoftware maintenance
700 1 $aFERNEDA, E.
700 1 $aANQUETIL, N.
700 1 $aTEIXEIRA, E. D'A.
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