• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction


Abstract

Software effort estimation is one of the most difficult tasks in software development whereas predictability is also of equal importance for strategic management. Accurate prediction of the actual cost that will be incurred in software development can be very beneficial for the strategic management. This study discusses the latest trends in software estimation focusing on analogy-based techniques to show how they have improved the accuracy for software effort estimation. It applies the standard deviation technique to the expected value of analogy-based estimates to improve accuracy. In more than 60 percent cases the applied technique of this study helped in improving the accuracy of software estimation by reducing the Magnitude of Relative Error (MRE). The technique is simple and it calculates the expected value of cost or time and then uses different confidence levels which help in making more accurate commitments to the customers.


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Cite this article

[IEEE Style]
M. A. Latif, M. K. Khan, U. Hani, "Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction," KSII Transactions on Internet and Information Systems, vol. 17, no. 5, pp. 1356-1376, 2023. DOI: 10.3837/tiis.2023.05.003.

[ACM Style]
Mohammad Ayub Latif, Muhammad Khalid Khan, and Umema Hani. 2023. Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction. KSII Transactions on Internet and Information Systems, 17, 5, (2023), 1356-1376. DOI: 10.3837/tiis.2023.05.003.

[BibTeX Style]
@article{tiis:45182, title="Using Standard Deviation with Analogy-Based Estimation for Improved Software Effort Prediction", author="Mohammad Ayub Latif and Muhammad Khalid Khan and Umema Hani and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2023.05.003}, volume={17}, number={5}, year="2023", month={May}, pages={1356-1376}}