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Software is unequivocally the foremost and indispensable entity in this technologically driven world. Therefore quality assurance, and in particular, software testing is a crucial step in the software development cycle. This paper presents an effective test selection strategy that uses a Spectrum of Complexity Metrics (SCM). Our aim in this paper is to increase the efficiency of the testing process by significantly reducing the number of test cases without having a significant drop in test effectiveness. The strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class, method, statement) and its characteristics.We use a series of experiments based on three applications with a significant number of mutants to demonstrate the effectiveness of our selection strategy.For further evaluation, we compareour approach to boundary value analysis. The results show the capability of our approach to detect mutants as well as the seeded errors.

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Software security is becoming a key quality concern as software applications are increasingly being used in untrustworthy computing environments such as the internet. Software is designed with the mindset of its functionalities and cost, where the focus is on the operational behavior while security concerns are neglected or marginally considered. As a result, software engineers build the software while lacking the knowledge about security and its effect on the system. This paper presents an approach for modeling the behavior of security threats using statecharts. The proposed approach introduces modular design for representing threats through the use of components and reusability. Through the focus on the behavior of an attack, software engineers can clearly define and understand security concerns as the application is being designed and developed. In addition, modeling security threats with statecharts makes it convenient to build a consistent semantic link between functional behaviors and security concerns.

The research proposed here was for an Arabic speech recognition application, concentrating on the Lebanese dialect. The system starts by sampling the speech, which was the process of transforming the sound from analog to digital and then extracts the features by using the Mel-Frequency Cepstral Coefficients (MFCC). The extracted features are then compared with the system's stored model; in this case the stored model chosen was a phoneme-based model. This reference model differs from the direct word template matching, where speech features that are extracted from the input are directly compared to the word templates. Each word template in the direct matching model was stored as a vector of feature parameters. Thus, when the vocabulary size of the ASR system becomes large, the memory size for the word template will become humongous. In contrast, the model used here was phoneme-like template matching. Word templates are stored as phoneme-like template parameters. Thus, the memory size for the word templates will not grow as fast as that of the direct matching model.