Call Graph Based Metrics to Evaluate Software Design Quality

Software defects prediction was introduced to support development and maintenance activities such as improving the software quality through finding errors or patterns of errors early in the software development process. Software defects prediction is playing the role of maintenance facilitation in terms of effort, time and more importantly the cost prediction for software maintenance and evolution activities. In this research, software call graph model is used to evaluate its ability to predict quality related attributes in developed software products. As a case study, the call graph model is generated for several applications in order to represent and reflect the degree of their complexity, especially in terms of understandability, testability and maintenance efforts. This call graph model is then used to collect some software product attributes, and formulate several call graph based metrics. The extracted metrics are investigated in relation or correlation with bugs collected from customers-bug reports for the evaluated applications. Those software related bugs are compiled into dataset files to be used as an input to a data miner for classification, prediction and association analysis. Finally, the results of the analysis are evaluated in terms of finding the correlation between call graph based metrics and software products\u27 bugs. In this research, we assert that call graph based metrics are appropriate to be used to detect and predict software defects so the activities of maintenance and testing stages after the delivery become easier to estimate or assess

Evaluation the Quality of Software Design by Call Graph based Metrics

The prediction of software defects was introduced to support development and maintenance activities to improve the software quality by finding errors early in the software development. It facilitates maintenance in terms of effort, time and more importantly the cost prediction for software evolution and maintenance activities. In this paper, we evaluate the quality related attributes in developed software products. The software call graph model is also used for several applications in order to represent and reflect the degree of their complexity in terms of understandability, testability and maintainability efforts. The extracted metrics are investigated for the evaluated applications in correlation with bugs collected from customers bug reports. Those software related bugs are compiled into datasets files to use as an input to a data miner for classification, prediction and association analysis. Finally, the analysis results is evaluated in terms of finding the correlation between software products bugs and call graph based metrics. We find that call graph based metrics are appropriate to detect and predict software defects so that the activities of testing and maintenance stages become easier to estimate or assess after the product delivery

Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study

The degree of dependencies among the modules of a software system is a key attribute to characterize its design structure and its ability to evolve over time. Several design problems are often correlated with undesired dependencies among modules. Being able to anticipate those problems is important for developers, so they can plan early for maintenance and refactoring efforts. However, existing tools are limited to detecting undesired dependencies once they appeared in the system. In this work, we investigate whether module dependencies can be predicted (before they actually appear). Since the module structure can be regarded as a network, i.e, a dependency graph, we leverage on network features to analyze the dynamics of such a structure. In particular, we apply link prediction techniques for this task. We conducted an evaluation on two Java projects across several versions, using link prediction and machine learning techniques, and assessed their performance for identifying new dependencies from a project version to the next one. The results, although preliminary, show that the link prediction approach is feasible for package dependencies. Also, this work opens opportunities for further development of software-specific strategies for dependency prediction.Comment: Accepted at ICSA 201