An analysis of the fault correction process in a large-scale SDL production model
Proceedings of the 25th International Conference on Software Engineering
Detecting and resolving semantic pathologies in UML sequence diagrams
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Resolving Race Conditions in Asynchronous Partial Order Scenarios
IEEE Transactions on Software Engineering
Inherent causal orderings of partial order scenarios
ICTAC'04 Proceedings of the First international conference on Theoretical Aspects of Computing
Scenario synthesis from imprecise requirements
SAM'04 Proceedings of the 4th international SDL and MSC conference on System Analysis and Modeling
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Three metrics were used to extract design information from existing code to identify structural stress points in a software system being analyzed: Di, an internal design metric which incorporates factors related to a module's internal structure; De, an external design metric which focuses on a module's external relationships to other modules in the software system; and D(G), a composite design metric which is the sum of Di and De. Since stress point modules generally have a high probability for being fault-prone, project managers can use the information to determine where additional testing effort should be spent and assign these modules to more experienced programmers if modifications are needed. To make the analysis more accurate and efficient, a design metrics analyzer (xMetrics) was implemented. In this study, we conducted experiments using xMetrics on part of a distributed software system, written in C, with a client-server architecture, and identified a small percentage of its functions as good candidates for fault-proneness. Files containing these functions were then validated by the real defect data collected from a recent major release to its next release for their fault-proneness. Normalized metrics values were also computed by dividing the Di, De, and D(G) values by the corresponding function size determined by non-blank and non-comment lines of code to study the possible impact of function size on these metrics. Results indicate that function size has little impact on the predictive quality of our design metrics in identifying fault-prone functions.