The study applies canonical correlation analysis to investigate the relationship between source code complexity and fault correction activity. Product and process measures collected during the development of a commercial real-time product provide the data for this analysis. Sets of variables represent source code complexity and fault correction activity. Significant canonical correlations along two dimensions support the hypothesis that source code complexity exerted a causal influence on fault correction activity during the system test phase of the real-time product. Interpretation of the two significant canonical correlations reveals relationships between the sets of variables that are not immediately apparent from their simple correlations.>

The current software market favors software development organizations that apply software quality models. Software engineers fit quality models to data collected from past projects. Predictions from these models provide guidance in setting schedules and allocating resources for new and ongoing development projects. To improve model stability and predictive quality, engineers select models from the orthogonal linear combinations produced using principal components analysis. However, recent research revealed that the principal components underlying source code measures are not necessarily stable across software products. Thus, the principal components underlying the product used to fit a regression model can vary from the principal components underlying the product for which we desire predictions. We investigate the impact of this principal components instability on the predictive quality of regression models. To achieve this, we apply an analytical technique for accessing the aptness of a given model to a particular application.>

The extreme risks of software faults in the telecommunications environment justify the costs of data collection and modeling of software quality. Software quality models based on data drawn from past projects can identify key risk or problem areas in current similar development efforts. Once these problem areas are identified, the project management team can take actions to reduce the risks. Studies of several telecommunications systems have found that only 4-6% of the system modules were complex /spl lsqb/LeGall et al. 1990/spl rsqb/. Since complex modules are likely to contain a large proportion of a system's faults, the approach of focusing resources on high-risk modules seems especially relevant to telecommunications software development efforts. A number of researchers have recognized this, and have applied modeling techniques to isolate fault-prone or high-risk program modules. A classification model based upon discriminant analytic techniques has shown promise in performing this task. The authors introduce a neural network classification model for identifying high-risk program modules, and compare the quality of this model with that of a discriminant classification model fitted with the same data. They find that the neural network techniques provide a better management tool in software engineering environments. These techniques are simpler, produce more accurate models, and are easier to use.>

By modeling the relationship between software complexity attributes and software quality attributes, software engineers can take actions early in the development cycle to control the cost of the maintenance phase. The effectiveness of these model-based actions depends heavily on the predictive quality of the model. An enhanced modeling methodology that shows significant improvements in the predictive quality of regression models developed to predict software changes during maintenance is applied here. The methodology reduces software complexity data to domain metrics by applying principal components analysis. It then isolates clusters of similar program modules by applying cluster analysis to these derived domain metrics. Finally, the methodology develops individual regression models for each cluster. These within-cluster models have better predictive quality than a general model fitted to all of the observations.>

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.