The Establishment and Application of Effort Regression Equation
Authors: Junguang Zhang
Year: 2008

To define the reliability and usability of the linearity regression relationship between software size and effort, statistics analysis method was used to validate the regression result and seek for the reliability interval of coefficient b. The application method of the regression result was discussed under the situation that the regression result is usable. By analysis, it shows the result of the linearity regression relationship is ideal and has a high confidence level. The application of the linearity regression equation also proves that this regression relationship is effective and it could be applied in actual software projects. Therefore the equation could be used exactly during effort estimation of software projects. Keywordssoftware size; effort; regression relationship; validation; reliability; forecast and control Software project effort is a basic parameter of software engineering and software project management which has a direct impact on the cost of the project and the arrangement of the project schedule. Modern research shows that the three major constraint conditions of project management are quality, cost and schedule , and the effort has direct impact on the cost and the schedule of the project, so the effort estimation of software project is a critical activity which has much to do with the success or failure of a software project. Effective effort estimation could make effective arrangement for the cost and the schedule which are the key constraint factors of the project, and provide practical foundation and standards for the subsequent monitoring and control. 1 LINEAR REGRESSION EQUATION Usually, when we know software size, we will often use the linear regression relationship between the size and effort of the organization during the effort estimation in order to obtain the effort value according to the size value. There are many methods could be used for us to obtain the linear regression equation between software size and effort . For example: The size and effort historical data of some organization are shown in table 1, which have been amended by expert judgment method according to the complexity factors and are comparable and consistent. Size unit: 1000 line (code lines, C + + programming); effort unit: person day. Table 1: size and effort comparison table We set y as the representative of the size and set x as the representative of the effort. Then we could get the linear regression equation by formula, tools or programming method as formula 1. y=-3656.86+72.47134 x (formula 1) The result obtained provides a good foundation for us to estimate the effort of software projects. But whether the regression effect is significant? How about the confidence interval and the reliability of the coefficient b? If the regression result is effective, how should we use this regression equation to get the software effort according to the software size and to predict and control? Now we will study the test and use of the linear regression equation obtained through statistical analysis, so that we could provide credibility for the use and promotion project x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 effort 390 515 148 398 511 1305 221 720 120 325 42