Determination of the size of a software project is challenging as well as crucial for both self-employed software developers and corporate businesses. That's why it is subjected to a lot of academic studies where it is discussed how to determine the size more accurately. Functional Size Measurement (FSM) is one the most popular measurement techniques for a software from the point of the delivered functionality. However, the aspects of know-how, the cost, time, and manual operation creates difficulties to apply FSM techniques. This study aims to solve these issues by automating the measurement process to approximate the functional size of a project using the COSMIC Functional Size Measurement. The end product of this study is called 'Cosmic APP' that utilizes the sequence diagram of a software after reverse engineering it from the given code using a third-party tool called 'SequenceDiagram'. The working principles, the estimation process, and the obtained results of 'Cosmic APP' are described thoroughly in this paper.

Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to develop empirical equations to predict Water Capturing Performance (WCP) and Sediment Release Efficiency (SRE) for Coanda type intakes. These equations were developed using 216 sets of experimental data. Intakes were tested under six different slopes, six screens, and three water discharges. In SRE experiments, sediment concentration was kept constant. Dimensionless parameters were first developed and then subjected to multicollinearity analysis. Then, nonlinear equations were proposed whose exponents and coefficients were obtained using the Genetic Algorithm method. The equations were calibrated and validated with 70 and 30% of the data, respectively. The validation results revealed that the empirical equations produced low MAE and RMSE and high R2 values for both the WCP and the SRE. Results showed outperformance of the empirical equations against those of MNLR. Sensitivity analysis carried out by the ANNs revealed that the geometric parameters of the intake were comparably more sensitive than the flow characteristics.

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