A study of differences by industry using factor models influencing software development estimates

Tsuyoshi Shida, Kazuhiko Tsuda


Recently, IoT and AI/machine learning have attracted attention, and software development has been a critical activity for the companies that use IT. The investment in IT has been increasing, and it varies with the industry. In addition, software development has become complex with the growing sophistication in the target applications; therefore, it is a challenging task for the software vendors to prepare an accurate estimate. Consequently, the estimates grossly deviate from the true value. In this paper, we propose a method based on the previous research that uses the factors related to productivity of software development to find factors that affect the estimation of man-hours. We analyzed the parameters among populations using two factors and simultaneous analysis of multiple populations from nine industries. We used two-factor models extracted from “the study of software estimation factors extracted using covariance structure analysis” and verified the method by applying five constraints, including factor load amount and error variance, simultaneously for the nine industries. As a result, it was possible to separate industries with large factor variance and those with small factor variance. Moreover, it was possible to separate industries with large correlation coefficient between factors and industries with small factor correlation coefficient. For industries with small variance of factors, the factors are consistent within the industry, and in industries with large correlation between factors; the relationship between the two factors is more relevant. In other words, we could find out the relationship of factors influencing software estimation for each industry type. In addition, the variance of these two factors and the correlation coefficient between the factors were grouped, and a cluster analysis was performed. It was found that there was a difference in the estimate for each group of Business-to-Business and Business-to-Customer industry groups. Based on these results, while preparing software estimates, IT vendors would capture the characteristics of the factors for each type of industry and clarify the influential factors of fluctuation by being conscious of the productivity fluctuation factors related to the two factors. 

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DOI: https://doi.org/10.5430/air.v7n2p34


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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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