Detecting quality defects: methods to improve product lifecycle cost-effectiveness in the railway domain

The increased complexity and ubiquity of cyber-physical systems in recent times demands for more efficient and cost effective techniques to analyze software and hardware correctness, as well as to assess their performance at a given time in the future. Two disciplines that deal with these aspects of system development are \textit{verification} and \textit{performance evaluation}. During this thesis work we focused in methods for improving quality in both of these areas in the context of railway safety-critical domain. Verifying a system means to prove or disprove that the system is the correct implementation of a \textit{specification}, often expressed as a collection of \textit{properties} -- the Requirements -- written in a given \textit{language}. In the railway safety-critical domain the requirements play a key role in the product lifecycle as the system is developed and verified according to them; they are often expressed in natural language -- which is flexible, but inherently ambiguous -- albeit the strong needs of clearness and precision of the context. The requirements have to abide to strict quality criteria and the requirement review is therefore a very important activity to indentify quality defects and it is traditionally performed manually. Rule-based natural language processing (NLP) techniques have been developed to automatically perform this task. However, the literature is lacking empirical studies on the application of these techniques in industrial settings. This thesis mainly focuses on investigating to which extent NLP can be practically applied to detect defects in the requirements documents of a railway signalling manufacturer. The contribution is in carrying out one of the first works in which NLP techniques for defect detection are applied on a large set of industrial requirements annotated by domain experts. We contribute with a comparison between traditional manual techniques used in industry for requirements analysis, and analysis performed with NLP. Our experience shows that several discrepancies can be observed between the two approaches. The analysis of the discrepancies offers hints to improve the capabilities of NLP techniques with company specific solutions, and suggests that also company practices need to be modified to effectively exploit NLP tools. For what concerns the \textit{performance evaluation} area we had the opportunity to focus on the system availability in the context of a different project of the laboratory. With the increased city population, the integration of public and private transport flows introduces new challenges, especially in urban transport. As it is often the case in scientific and engineering problems, the object of study is a \textit{model} of the system, rather than the system itself. We provide one modeling and analysis method using stochastic Time Petri Nets for those city intersections where public and private transport flows integration is often cause of traffic congestion leading to train delays and even run deletion. The use of the STPN instead of simulation techniques provides a more effective way to set timing for traffic lights and train timetables in order to improve system availability.