Earticle

Random Testing(RT) and its derivatives such as Adaptive Random Testing (ART ) are active and important research topics in software testing, which have also a niche in practical settings due to the merits they offers, e.g. fault-detection capacities at low cost, ease of implementation, reliability estimation, facility for automation and so forth. Inspired by these advantages, we elieve the idea behind random testing can be worthwhile and attractive for testing aspectoriented programs since current research on testing of AOP, especially automated has not been adequately performed and is still in infancy. In this paper, we propose a preliminary approach to automated random testing of aspect-oriented programs, which are becoming an important part of software engineering theory and practice. This paper also includes a survey of applicable testing techniques and discussion of established testing methods in both area of Aspect-Oriented Programming (AOP) and Random Testing (RT).

Software has become responsible for most of the critical functions of complex systems. Software safety is the notion that software will execute within a system context without contributing to hazards. Software for safety-critical systems must deal with the hazards identified by safety analysis in order to make the system safe. Software safety is a composite of many criteria. Existing software quality models like McCall’s and Boehm’s and ISO 9126 are inadequate in addressing the software safety issues of real time safety-critical embedded systems. At present there does not exist any standard model that comprehensively addresses the factors, criteria and metrics (FCM) approach of the quality models in respect of software safety. This paper proposes a new model for software safety based on the McCall’s software quality model that specifically identifies the criteria corresponding to software safety in safety critical applications. This framework is then applied to a prototype safety-critical system viz. a software–controlled Road Traffic Control System (RTCS) commonly used in city traffic, to validate its utility.

The design of the state pattern is analyzed and the notion of stability of a design is proposed. Mathematical models and ideas from numerical analysis are used to derive a stable design for the state pattern. The stability of the design is illustrated by considering two canonical examples from the OO literature.

This paper discusses the use of new graph structural genetic programming for automatic programming, which creates finite state machines (FSM) by evolution. Generally, FSM must define their transition rules for all combinations of states and possible inputs, thus the FSM program will become large and complex when the number of states and inputs is large. In our work, the nodes are connected by trajectory information sets, so it is possible that only the essential problem’s behavior obtained in the current situation are used in the network flow, and it can determine an action by not only the current, but also the past information. In addition, the proposed algorithm enhances evolutionary process by using fitness inheritance technique. Constraining the depth of genetic programming tree is one of the ways to overcome its bloat problem. Finally, fitness inherent is used when fitness evaluation is computationally expensive. Fitness inherent is based on averaging; therefore it reflects some assumptions of smoothness in the search space

Legacy systems modernization is one of the most pressing issues for enterprise organizations. The continuous emergence of new technologies that are proven to be more robust, scalable, and maintainable, such as those using the Service Oriented Architectures (SOA), is currently attracting a lot of legacy systems modernization stakeholders. SOA legacy system modernization projects have induced a lot of researchers to address the full modernization project life cycle starting from architecture reconstruction, code analysis, modernization strategy selection, until eventually the actual strategy to be used in modernization is selected. Such strategy selection could depend on the component/module type of the legacy system (user interface, data layer, business functionality layers) and would also depend upon the platform, programming language used in the legacy system (Mainframe, Windows VC++, .etc), as well as the type of strategy used to modernize the system. Since there are a lot of characteristics that control the selection of a modernization strategy, the modernization selection process gets tougher and tougher with the increase of such characteristics. Accordingly, we are presenting a modernization selection process along with a decision making tool that handles much of those characteristics altogether using decision theory to come up with the most optimal strategy to be used in modernizing the legacy systems in question.

This paper presents a method for optimizing software testing efficiency by identifying the most critical path clusters in a program. We do this by developing variable length Genetic Algorithms that optimize and select the software path clusters which are weighted in accordance with the criticality of the path. Exhaustive software testing is rarely possible because it becomes intractable for even medium sized software. Typically only parts of a program can be tested, but these parts are not necessarily the most error prone. Therefore, we are developing a more selective approach to testing by focusing on those parts that are most critical so that these paths can be tested first. By identifying the most critical paths, the testing efficiency can be increased.