A predictive analysis approach using linear regression to estimate software effort
Resumo
Tomar decisões com um nível altamente incerto é um problema crítico na área de engenharia de software. A previsão da qualidade do software requer alta ferramentas precisas e experiência de alto nível. Modelos preditivos baseados em IA, no Por outro lado, são ferramentas úteis com um grau preciso que ajudam a tomar decisões aprendendo com dados passados. Neste estudo, construímos uma estimativa de esforço de software modelo para prever o esforço antes do ciclo de vida de desenvolvimento do projeto, usando um modelo de regressão e também usando modelo de validação não-paramétrico um algoritmo de regressão de Knn.
Referências
Boehm, B., Abts, C., and Chulani, S. (2000). Software development cost estimation approaches – a survey. Ann. Softw. Eng., 10(1-4):177–205.
Erc¸elebi Ayyıldız, T. and Can Terzi, H. (2017). Case study on software effort estimation. 7:103–107.
Halkjelsvik, T. and Jørgensen, M. (2011). From origami to software development: A review of studies on judgment-based predictions of performance time. 138:238–71.
Kitchenham, B., Pfleeger, S. L., McColl, B., and Eagan, S. (2002). An empirical study of maintenance and development estimation accuracy. Journal of Systems and Software, 64(1):57 – 77.
Lee-Post, A., Cheng, C. H., and Balakrishnan, J. (1998). Software development cost estimation: Integrating neural network with cluster analysis. 34:1–9.
Mehedi Hassan Onik, M., Ahmmed Nobin, S., Ferdous Ashrafi, A., and Mohmud Chowd_x005fhury, T. (2018). Prediction of a Gene Regulatory Network from Gene Expression Profiles With Linear Regression and Pearson Correlation Coefficient. ArXiv e-prints.
Pandian, C. R. (2003). Software metrics: A guide to planning, analysis, and application.
Rodgers, J. and Nicewander, W. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59–66.
Trendowicz, A. and Jeffery, R. (2014). Software Project Effort Estimation: Foundations and Best Practice Guidelines for Success. Springer Publishing Company, Incorporated.
Wazlawick, R. (2013). ENGENHARIA DE SOFTWARE: CONCEITOS E PRATICAS. Elsevier Editora Ltda
Erc¸elebi Ayyıldız, T. and Can Terzi, H. (2017). Case study on software effort estimation. 7:103–107.
Halkjelsvik, T. and Jørgensen, M. (2011). From origami to software development: A review of studies on judgment-based predictions of performance time. 138:238–71.
Kitchenham, B., Pfleeger, S. L., McColl, B., and Eagan, S. (2002). An empirical study of maintenance and development estimation accuracy. Journal of Systems and Software, 64(1):57 – 77.
Lee-Post, A., Cheng, C. H., and Balakrishnan, J. (1998). Software development cost estimation: Integrating neural network with cluster analysis. 34:1–9.
Mehedi Hassan Onik, M., Ahmmed Nobin, S., Ferdous Ashrafi, A., and Mohmud Chowd_x005fhury, T. (2018). Prediction of a Gene Regulatory Network from Gene Expression Profiles With Linear Regression and Pearson Correlation Coefficient. ArXiv e-prints.
Pandian, C. R. (2003). Software metrics: A guide to planning, analysis, and application.
Rodgers, J. and Nicewander, W. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1):59–66.
Trendowicz, A. and Jeffery, R. (2014). Software Project Effort Estimation: Foundations and Best Practice Guidelines for Success. Springer Publishing Company, Incorporated.
Wazlawick, R. (2013). ENGENHARIA DE SOFTWARE: CONCEITOS E PRATICAS. Elsevier Editora Ltda
Publicado
22/08/2018
Como Citar
ESTEVES, Antogio; MEDEIROS, Leonardo.
A predictive analysis approach using linear regression to estimate software effort. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 18. , 2018, Aracaju.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 396-400.
