In software engineering, system construction typically starts from a requirements specification that has been engineered from raw requirements in a natural language. The specification is used to derive intermediate requirements models such as structured or object-oriented models. Throughout the stages of system construction, these artefacts will be used as reference models. In general, in order to derive a design specification out of the requirements, the entire set of requirements specifications has to be analysed. Such models at best only approximate the raw requirements since these design models are derived as a result of the abstraction process according to the chosen software development methodology, and subjected to the expertise, intuition, judgment and experiences of the analysts or designers of the system. These abstraction models require the analysts to elicit all useful information from the requirements, and there is a potential risk that some information may be lost in the process of model construction. As the use of natural language requirements in system construction is inevitable, the central focus of this study was to use requirements stated in natural language in contrast to any other requirements representation (e.g. modelling artefact). In this thesis, an approach that avoids intermediate requirements models, and maps natural language requirements directly into architectural constructs, and thus minimises information loss during the model construction process, has been defined. This approach has been grounded on the adoption of a component model that supports incremental composition. Incremental composition allows a system to be constructed piece by piece. By mapping a raw requirement to elements of the component model, a partial architecture that satisfies that requirement is constructed. Consequently, by iterating this process for all the requirements, one at a time, the incremental composition to build the system piece by piece directly from the requirements can be achieved. In software engineering, system construction typically starts from a requirements specification that has been engineered from raw requirements in a natural language. The specification is used to derive intermediate requirements models such as structured or object-oriented models. Throughout the stages of system construction, these artefacts will be used as reference models. In general, in order to derive a design specification out of the requirements, the entire set of requirements specifications has to be analysed. Such models at best only approximate the raw requirements since these design models are derived as a result of the abstraction process according to the chosen software development methodology, and subjected to the expertise, intuition, judgment and experiences of the analysts or designers of the system. These abstraction models require the analysts to elicit all useful information from the requirements, and there is a potential risk that some information may be lost in the process of model construction. As the use of natural language requirements in system construction is inevitable, the central focus of this study was to use requirements stated in natural language in contrast to any other requirements representation (e.g. modelling artefact). In this thesis, an approach that avoids intermediate requirements models, and maps natural language requirements directly into architectural constructs, and thus minimises information loss during the model construction process, has been defined. This approach has been grounded on the adoption of a component model that supports incremental composition. Incremental composition allows a system to be constructed piece by piece. By mapping a raw requirement to elements of the component model, a partial architecture that satisfies that requirement is constructed. Consequently, by iterating this process for all the requirements, one at a time, the incremental composition to build the system piece by piece directly from the requirements can be achieved.

Natural Language (NL) requirements play a significant role in specifying the system design, implementation and testing processes. Nevertheless, NL requirements are generally syntactically ambiguous and semantically inconsistent. Issues with NL requirements can result into inaccurate and preposterous system design, implementation and testing. Moreover, informal nature of NL is a major hurdle in machine processing of system requirements specifications. To confront this problem, a requirement template is introduced, based on controlled NL to produce deterministic and consistent representation of the system. The ultimate focus of this thesis is to generate test cases from system specifications driven from requirements communicated in natural language. Manual software systems testing is a labour intensive, error prone and high cost activity. Traditionally, model-driven test generation approaches are employed for automated testing. However, system models are created manually for test generation. The test cases generated from system models are not generally deterministic and traceable with individual requirements. This thesis proposes an approach for software system testing based on template-driven requirements. This systematic approach is applied on the requirements elicited from system stakeholders. For this purpose natural language processing (NLP) methods are used. Using NLP approaches, useful information is extracted from controlled NL requirements and afterwards the gathered information is processed to generate test scenarios. Our inceptive observation exhibits that this method provides remarkable gains in terms of reducing the cost, time and complexity of requirements based testing.

Ensuring privacy and confidentiality concerns of data owners is an important aspect of a secured information system. This is particularly important for integrated systems, which allow data exchange across organizations. Governments, regulatory bodies and organizations provide legislations, regulations and guidelines for information privacy and security to ensure proper data handling. These are usually specified in natural language formats, contain default requirements and exceptions, and are often ambiguous. In addition, interacting concerns, which are often multilayered and from different stakeholders, e.g., jurisdictions, need to be considered in software development. Similar to other security concerns, analysis of confidentiality concerns should be integrated into the early phase of software development in order to facilitate early identification of defects - incompleteness and inconsistencies, in the requirements. This dissertation presents research conducted to develop a method to detect these defects using goal models which support defaults and exceptions. The goal models are derived from annotations of the natural language sources. A prototype tool is also developed to support the method. The evaluations conducted indicate the method and tool provide benefits, including distinguishing requirement interferences and conflicts, exception handling, and navigation between annotated documents and the goal models. Although current limitations of the method include a manual user driven annotation step, the method provides features that assist in early analysis of confidentiality requirements from natural language sources.

Capturing client’s needs and expectations for a product or service is an important problem in software development. Software requirements are normally captured in natural language and mostly they are unstructured which makes it difficult to automate the process of going from software requirements to the executable code. A big hurdle in this process is the lack of consistency and standardization in software requirements representation. Thus, the aim of the thesis is to present a method for transforming natural language requirement text into ontology. It is easy to store and retrieve information from ontology as it is a semantic model, and it is also easy to infer new knowledge from it. As it is clear from the aim of this work, the main component of our research was software requirements, so there was a need to investigate and decide the types of requirements to define the scope of this research. We selected INCOSE guidelines as a benchmark to scrutinize the properties which we desired in the Natural Language Requirements. These natural language requirements were used in the form of user stories as the input of the transformation process. We selected a combination of two methods for our research i.e. Literature Review and Design Science Research. The reason for selecting these methods was to obtain a good grip on existing work going on in this field and then to combine the knowledge to propose new rules for the requirements to ontology transformation. We studied different domains during literature review such as Requirements Engineering, Ontologies, Natural Language Processing, and Information Extraction. The gathered knowledge was then used to propose the rules and the flow of their implementation. This proposed system was named as “Reqtology”. Reqtology defines the process, from taking the requirements in form of user stories, to extracting the useful information based on the rules and then classifying that information so that it can be used to form ontologies. The workflow consists of a 6-step process which starts from input text in form of user stories and at the end provides us entities which can be used for ontologies formation.

Software testing is becoming more prominent within the automotive industry due to more complex systems, and functions are implemented in the vehicles. The vehicles in the future will have the functionality to manage different levels of automation, which also means that vehicles driven by humans will have more supportive functionality to increase safety and avoid accidents. These functionalities result in a massive growth in the number of test scenarios to indicate that the vehicles are safe, and this makes it impossible to continue performing the tests in the same way as it has been done until today. The new conditions require that the test scenarios and Test Cases both be generated and executed automatically. In this thesis, an investigation and evaluation are performed to analyze the Automatic Test Case Generation methods available for inputs from Natural Language Requirements in an automotive industrial context at NEVS AB. This study aims to evaluate the NAT2TEST strategy by replacing the manual method and obtain a similar or better result. A comparative analysis is performed between the manual and automated approaches for various levels of requirements. The results show that utilizing this strategy in an industrial scenario can improve efficiency if the requirements to be tested are for well-documented lower-level requirements.