Off-nominal behaviors (ONBs) have been a major concern in the areas of embedded systems and safety-critical systems. To address ONB problems, some researchers have proposed model-based approaches that can expose ONBs by analyzing natural language requirements documents. While these approaches produced promising results, they require a lot of human effort and time. In this paper, to reduce human effort and time, we propose a combinatorial-based approach, Combinatorial Causal Component Model (Combi-CCM), which uses structured requirements patterns and combinations generated using the IPOG algorithm. We conducted an empirical study using several requirements documents to evaluate our approach, and our results indicate that the proposed approach can reduce human effort and time while maintaining the same ONB exposure ability obtained by the control techniques.

Requirements are typically specified on the assumption that the system's operating environment will behave in what is considered to be an expected and nominal manner. When gathering requirements, one concern is whether the requirements are too incomplete to account for every possible, unintended, off-nominal behavior (ONB) that the operating environment can create in the system. In this paper, we present a semi-automated approach, based on the causal component model (CCM), which can expose, within a set of requirements, whether ONBs can result in undesired system states. We demonstrate how the CCM approach exposes and helps address potential off-nominal behavior problems in a set of requirements that represents a real-world product. Our case study shows that the approach can expose susceptibility to ONBs and can supply information in correcting requirements.

Adequate requirements validation could prevent errors from propagating into later development phase, and eventually improve the quality of software systems. However, often validating textual requirements is difficult and error prone. We develop a feedback-based requirements validation methodology that provides an interactive and systematic way to validate a requirements model. Our approach is based on the notion of querying a model, which is built from a requirements specification, with scenario questions, in order to determine whether the model's behavior satisfies the given requirements. To investigate feasibility of our approach, we implemented a Scenario Question Query Engine (SQ2E), which uses scenario questions to query a model, and performed a preliminary case study using a real-world application. The results show that the approach we proposed was effective in detecting both expected and unexpected behaviors in a model. We believe that our approach could improve the quality of requirements and ultimately the quality of software systems.