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With the gradual maturity of component oriented software development method, component-based software evolution technology has become hot research in academia and industry. Although many evolution rules are designed, they rarely consider component type-mismatched problem in evolution rules. This has led to evolution rules that often run error in software evolution execution. Hence, focusing on the mismatch problem of component type in software evolution, this paper addresses various evolution rules with condition constrains to support component type matching. First, we use the bigraph theory to model the software architecture and employ bigraph term language to describe the basic component evolution operations. Second, we join type system into the term language and use the type term language to express the condition constraints on position and connection for component evolution rules. These condition constraints can guarantee the type-matched among components that participate in software evolution. Furthermore, we show that the component type-matched still kept during a number of different evolution rules are used in the whole software evolution reaction system. Finally, two cases study of evolution progress of ATM system and tourism information system are presented. Two cases illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Software testing is an essential activity to ensure the quality of software systems. Combinatorial testing is a method that facilitates the software testing process; it is based on an empirical evidence where almost all faults in a software component are due to the interaction of very few parameters. The test generation problem for combinatorial testing can be represented as the construction of a matrix that has certain properties; typically this matrix is a covering array. Covering arrays have a small number of tests, in comparison with an exhaustive approach, and provide a level of interaction coverage among the parameters involved. This paper presents a repository that contains binary covering arrays involving many levels of interaction. Also, it discusses the importance of covering array repositories in the construction of better covering arrays. In most of the cases, the size of the covering arrays included in the repository reported here are the best upper bounds known, moreover, the files containing the matrices of these covering arrays are available to be downloaded. The final purpose of our Binary Covering Arrays Repository (BCAR) is to provide software testing practitioners the best-known binary test-suites. [ABSTRACT FROM AUTHOR]

In this paper a platform is presented, that given a stochastic context-free grammar (SCFG), automatically outputs the description of the parser in synthesizable hardware description language (HDL) which can be downloaded in an Field Programmable Gate Arrays (FPGA) board. Initially, according to our methodology the SCFG is augmented with attributes which store the probability values and can be evaluated through corresponding stack actions. The architecture of the produced system is based on a proposed extension of Earley's parallel algorithm, which given an input string, generates the parse trees in the form of an AND-Or parse tree. This AND-or parse tree is then traversed using a proposed tree traversal technique in order to execute the corresponding actions in the correct order, so as to compute the necessary probabilities. The platform is suitable for embedded systems applications where a natural language interface is required or in pattern recognition tasks. The parser generated by the presented platform has been tested for various SCFGs and compared to software approaches. The performance comparison is one to two orders of magnitude in favor of the presented hardware, compared to previous software approaches, depending on the application, the input string length and the number of produced trees. [ABSTRACT FROM AUTHOR]

Program matching refers to the mapping between equivalent codes written in different languages - including high-level and low-level languages. This equivalence is useful for some software engineering scenarios such as determining whether rewritten code is correct, which version of a program is being used, and whether a malware is present in the program. In the present work, we propose a novel approach to solve the executable code traceability by using program code analysis and artificial neural networks. From the program code analysis we obtained execution behavior properties of the codes, and from the artificial neural networks we judge about their correspondence. Our evaluation using real code examples shows an acceptable correspondence rate between 62% and 100% with the very low rate of 4% false positives. [ABSTRACT FROM AUTHOR]

The process of engineering software-intensive systems that comply with their Certification and Accreditation (C&A) requirements involves many critical decision-making activities for the related stakeholders. Considering the exhaustive nature of C&A activities together with the complexity of software-intensive systems, effective decision making relies heavily on the ways to understand and structure the problem domain concepts concerning decision points for interpretation, applicability, scope, evaluation, and impact of the enforced C&A requirements. These decision points are further complicated by natural language specifications of inherently non-functional C&A requirements scattered across multiple regulatory documents with complex interdependencies at different levels of abstractions in the organizational hierarchy, which often result in subjective interpretations and non-standard implementations of the C&A process. To address these issues, we define a systematic methodology using novel techniques from software Requirements Engineering (RE) and knowledge engineering for understanding and structuring the problem domain concepts based on a uniform representation format that promotes common understanding among stakeholders. Specifically, we use advanced ontological engineering techniques driven by theoretical RE foundations to systematically elicit, model, understand, and analyze problem domain concepts concerning significant and difficult decision points throughout the C&A process. We demonstrate the appropriateness of our methodology in creating decision support problem domain ontology using several examples derived from our experiences on automating the Department of Defense Information Technology Security C&A Process (DITSCAP). [ABSTRACT FROM AUTHOR]