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The more complex a software system is, the more likely it is that programmers will make mistakes that introduce faults which can lead to execution failures. A risk in a software system can be viewed as a potential problem, and a problem is a risk that has manifested. In order to reduce the risk of software operations, code which has the potential to cause problems has to be identified so that necessary actions (e.g., performing a more thorough testing on such code) can be taken to prevent any such problems from occurring. Consequently, this can help programmers detect faults in the software before it is deployed and reduce the overall maintenance code. In this paper, we propose a static and a dynamic risk model using metrics collected based on the source code; more specifically, metrics which are either related to the static structure of the source code or the dynamic test coverage of the code. The computation of the risk of code is automated at different granularity levels ranging from basic blocks to functions. An experiment to demonstrate the feasibility of using our method is reported. High risk code, so identified by our method, can be integrated with information collected from other software quality assurance practices to further ensure the safe operation of software applications.

The goals of developing systems better, faster, and cheaper continue to drive software engineering practitioners and researchers to investigate software engineering methodologies. In requirements engineering, the focus has been on modeling the software engineering process and products for systems that are being built from scratch. As the size and complexity of systems continues to g row the use of commercial off the shelf (COTS) components is being viewed more and more as a promising, and also perhaps inevitable, solution. The effective use of COTS components, however, requires a systematic approach that provides a set of concepts for modeling the subject matter, a set of guidelines for using such concepts, and tool support to assist the developer.In this paper, we present a COTS-Aware Requirements Engineering (CARE) approach that explicitly supports the use of COTS components. Our CARE approach is knowledge based, has a defined process, and is agent-and goal-oriented. In particular, we present part of our meta-model for the knowledge base and a prototype of the CARE Assistant Tool. Our CARE approach is validated using a part of a Digital Library System example.