Narrow Results

Software effort estimation is an important and integral part of software development life cycle of any project. However, cost, time and manpower estimation is required prior to implementation of the project. The objective of this work is to explore the possibilities of application of Artificial Neural Network (ANN) as a tool for predicting software development effort. We proposed an ANN model for predicting software development effort. A multilayer feed forward network is trained using back-propogation algorithm and demonstrated to be suitable. This study used the training and validation data, which is randomly selected from the data repository of 650 projects [8]. The experimental results indicate that the Mean Absolute Relative Error (MARE) is 0.261 of ANN model and shows that ANN model is a competitive model for predicting software development effort.

Software size estimation at the early analysis phase of software development lifecycle is crucial for predicting the associated effort and cost. Analysis phase captures the functionality addressed in the software to be developed in object-oriented software development life-cycle. Unified modeling language captures the functionality of the software at the analysis phase based on use case model. This paper proposes a new method named as use case model function point to estimate the size of the object-oriented software at the analysis phase itself. While this approach is based on use case model, it also adapts the function point analysis technique to use case model. The various features such as actors, use cases, relationship, external reference, flows, and messages are extracted from use case model. Eleven rules have been derived as guidelines to identify the use case model components. The function point analysis components are appropriately mapped to use case model components and the complexity based on the weightage is specified to calculate use case model function point. This proposed size estimation approach has been evaluated with the object-oriented software developed in our software engineering laboratory to assess its ability to predict the developmental size. The results are empirically analysed based on statistical correlation for substantiating the proposed estimation method.

Software size estimation is an important aspect in software development projects because poor estimations can lead to late delivery, cost overruns and possibly project failure. Backfiring is a popular technique for sizing and predicting the volume of source code by converting the function point metric into source lines of code mathematically using conversion ratios. While this technique is popular and useful, there is a high margin of error in backfiring. This research introduces a new method to reduce this margin of error. Neural networks and fuzzy logic in software prediction models have been demonstrated in the past to have improved performance over traditional techniques. For this reason, a neuro-fuzzy approach is introduced to the backfiring technique to calibrate the conversion ratios. This paper presents the neuro-fuzzy calibration solution and compares the calibrated model against the default conversion ratios currently used by software practitioners.