International Journal of Software Engineering and Knowledge Engineering

The existing techniques for software testing can be used to perform only a particular type of testing, and moreover proficiency is required to write the automation test scripts using these techniques. This paper proposes a novel software testing approach using Aspect-Oriented Programming (AOP) that alone suffices for carrying out most of the types of software testing and thus obliterates the need of using distinctive tools for different types of testing. Nevertheless, AOP is a new programming paradigm and not all testers have the proficiency of working with it. Hence, a domain-specific language named Testing Aspect Generator Language (TAGL) was developed which has got a very low learning curve. Using TAGL, testers can write the testing code in the form of natural language-like statements. Further, the lexical analyzer and parser, written using lex and yacc, convert the TAGL statements into actual testing code in the form of AOP. The proposed approach was applied for the testing of widely used open source projects and remarkable bugs were detected into them. A detailed comparison as to how our approach is effective than the conventional testing techniques is provided.

Agile methods have provided significant contributions to Software Engineering. This work presents a new process for Software Requirements Specification, integrating Agile Properties and regulated environments, such as aviation, medical, nuclear and automotive, among others. The Software in Regulated Environments (SRE) involves plan-driven methods with needed documentation to ensure safety, reliability, security, and discipline. This paper proposes a balance between agile and plan-driven methods. We define a new process, which explores and investigates the usage of agile methods in SRE. The scope of this paper is Requirements Engineering, which is considered as a set of activities involved in the management, elicitation, documentation, and maintenance of requirements. The Adile Requirements Specification (ARES) process contains four methods, 13 activities, and some required artifacts to ensure compliance with the following six relevant Software Standards for regulated environments: RTCA DO-178C, IEC 62304:2015, ECSS-E-ST-40C, IEC 61508-3, ISO/IEC/IEEE 12207, and IAEA SSG-39. The process evaluation was performed using two experiments: a Cockpit Display System (CDS) and a Healthcare Information System (HIS). These experiments were measured with appropriate metrics to ensure improvements in Software Requirements Specification and traceability among artifacts. The experimental results revealed that the ARES process works better than the original Scrum for Software in Regulated Environments. The ARES process can also be integrated with traditional software life cycles (Waterfall, V, and Incremental and Iterative), when applied in the Requirements Engineering phase.

Inadequate documentation of software design has been known to be a barrier for developers. Interestingly, several relevant object-oriented systems have their design documented using key classes, which are meant to represent key concepts of the systems. In order to fill the gap of under-documented design, we present Keecle, an approach for detecting a predefined number of key classes in a semi-automatic way. The main challenge is to reduce the space of potentially thousands of classes to just a few representatives of the main concepts of a system, while maintaining high precision. The approach is evaluated with 13 systems in order to assess its correctness. The ground-truth is obtained either from the original documentation, or from third-party, or from the respective developers. The results were analyzed in terms of precision and recall, and have shown to be superior compared to the state-of-the-art approach. In order to evaluate if key classes are more critical from the design point of view, we evaluated whether they are associated with cohesion and coupling metrics. We found that although key classes, in general, are critical from the point of view of design, there are other classes that are also critical, suggesting that being aware of key classes encompass information not available in structural metrics, and could be useful as a additional facet for design assessment.

Counterexample-guided abstraction refinement (CEGAR) is an extremely successful methodology for combating the state-space explosion problem in model checking. State-space explosion problem is more serious in the field of stochastic model checking, and it is still a challengeable problem to apply CEGAR in stochastic model checking effectively. In this paper, we formalize the problem of applying CEGAR in stochastic model checking, and propose a novel CEGAR framework for it. In our framework, the abstract model is presented by a quotient probabilistic automaton by making a set of variables or latches invisible, which can distinguish more degrees of abstraction for each variable. The counterexample is described by a diagnostic sub-model. Validating counterexample is performed on diagnostic loop paths, and the directed explicit state-space search algorithm is used for searching diagnostic loop paths. Sample learning, particle swarm optimization algorithm (PSO) and some effective heuristics are integrated for refining abstract model guided by invalid counterexample. A prototype tool is implemented for the framework, and the feasibility and efficiency are shown by some large cases.

Attaining effective communication within and across organizational units is among the most critical challenges for success in software development organizations. This paper presents a novel model, supporting analysis of problems in inter-departmental communication events. The model was developed and designed based on industrial needs emphasizing flexibility, applicability and scalability. The model covers central communication aspects in order to provide a useful approximation of communication problems rather than in-depth modeling on message-by message basis. Other event-specific information, such as costs, can then be attached to enrich analysis and understanding. To exemplify and evaluate the model and collect feedback from industry, it was applied to 16 events at a Swedish automotive manufacturer where communication between two departments had broken down during development of software-intensive systems. The evaluation showed that the model helped structure and conduct systematic data collection and analysis of dysfunctional communication patterns. We found that insufficient understanding of the matters being communicated was prevalent, but also more specifically, requirements were insufficiently balanced, detailed and specified over the full system development cycle. Besides, the long-term cost for the company was analyzed in depth for each event, yielding a total estimated cost for the analyzed communication events of 11.2MUS$.

Web API recommendations have recently been studied extensively. However, recommending an API for a service is different than service intelligence. Web API automatic calls are widely used in question–answer dialog applications and service-composed workflow systems to achieve intelligent services. To finish an automatic Web API call not only requires the Web API ID, but also its input parameters. In this paper, we propose an end-to-end Web API automatic calls approach, named WAAC, that translates a goal’s natural language sentences directly to the Web API invoking sequences including its ID and parameters. This end-to-end approach based on the seq2seq encoder–decoder framework, adopts character-level RNN for the Chinese sentences and introduces a copying mechanism to retrieve API parameters. To train the network, a Chinese version dataset of over 1 million natural sentences and API invoking sequence pairs are generated with some manually labeled data and 72 real Web API invoking logs. Experiments obtain a 96% precision on predicting API invoking sequences and show that the character-level RNN and copying mechanism both contribute considerably to achieving a high precision Web API automatic call system for goal-oriented services.

There have been concerting policy and legal initiatives to mitigate the privacy harm resulting from badly designed software technology. But one main challenge to realizing these initiatives is the difficulty in translating proposed principles and regulations into concrete and verifiable evidence in technology. This is partly due to the lack of systematic techniques and tools to address privacy in the software design, hence making it difficult for the designer to measure disclosure risk in a more intuitive way, taking into account the privacy objective that matters to each end user. To bridge this gap, we propose a framework for verifying the satisfaction of user privacy objectives in software design. Our approach is based on the (un)awareness that users acquire when information is disclosed, as it relates to the communication properties of objects in a design. This property is used to determine the expected privacy utility that users will derive from the design for a specified privacy objective. We demonstrate through case studies how this approach can help designers determine which design decision undermines users’ privacy expectations and better design alternatives.