A neural network modeling methodology for the detection of high-risk programs

The profitability of a software development effort is highly dependent on both timely market entry and the reliability of the released product. To get a highly reliable product to the market on schedule, software engineers must allocate resources appropriately across the development effort. Software quality models based upon data drawn from past projects can identify key risk or problem areas in current similar development efforts. Knowing the high-risk modules in a software design is a key to good design and staffing decisions. A number of researchers have recognized this, and have applied modeling technqiues to isolate fault-prone or high-risk program modules early in the development cycle. Discriminant analytic classification models have shown promise in performing this task. We introduce a neural network classification model for identifying high-risk program modules, and we compare the quality of this model with that of a discriminant classification model fitted with the same data. We find that the neural network techniques provide a better management tool in software engineering environments