An approach for creating sentence patterns for quality requirements

Requirements are usually categorized in functional requirements (FRs) and quality requirements (QR). FRs describe "things the product must do" while QRs describe "qualities the product must have". Besides the definition, classification, and representation problems identified by Glinz, there are two further problems with current definitions of quality requirements: (i) the definitions are imprecise and thus difficult to understand and apply, and (ii) the definitions provide no guidance or support for their application in a given organizational context. To tackle these two problems, we propose an approach that - given a quality attribute (e.g., performance) as input - provides a means to specify quality requirements by sentence patterns regarding this quality attribute. In this paper, we contribute a detailed presentation and description of our approach and a discussion of our lessons learnt while instantiating it for performance requirements. Additionally, we give guidance on how to apply our approach for further quality attributes. Through this approach, we aim at encouraging researchers to help us improve the precision of definitions for quality requirements and support practitioners in eliciting and documenting better quality requirements

Automatic Transformation of Natural to Unified Modeling Language: A Systematic Review

Context: Processing Software Requirement Specifications (SRS) manually takes a much longer time for requirement analysts in software engineering. Researchers have been working on making an automatic approach to ease this task. Most of the existing approaches require some intervention from an analyst or are challenging to use. Some automatic and semi-automatic approaches were developed based on heuristic rules or machine learning algorithms. However, there are various constraints to the existing approaches of UML generation, such as restriction on ambiguity, length or structure, anaphora, incompleteness, atomicity of input text, requirements of domain ontology, etc. Objective: This study aims to better understand the effectiveness of existing systems and provide a conceptual framework with further improvement guidelines. Method: We performed a systematic literature review (SLR). We conducted our study selection into two phases and selected 70 papers. We conducted quantitative and qualitative analyses by manually extracting information, cross-checking, and validating our findings. Result: We described the existing approaches and revealed the issues observed in these works. We identified and clustered both the limitations and benefits of selected articles. Conclusion: This research upholds the necessity of a common dataset and evaluation framework to extend the research consistently. It also describes the significance of natural language processing obstacles researchers face. In addition, it creates a path forward for future research

Automated energy compliance checking in construction

Automated energy compliance checking aims to automatically check the compliance of a building design – in a building information model (BIM) – with applicable energy requirements. A significant number of efforts in both industry and academia have been undertaken to automate the compliance checking process. Such efforts have achieved various levels of automation, expressivity, representativeness, accuracy, and efficiency. Despite the contributions of these efforts, there are two main gaps in existing automated compliance checking (ACC) efforts. First, existing methods are not fully-automated and/or not generalizable across different types of documents. They require different degrees of manual efforts to extract requirements from text into computer-processable representations, and matching the concept representations of the extracted requirements to those of the BIM. Second, existing methods only focused on code checking. There is still a lack of efforts that address contract specification checking. To address these gaps, this thesis aims to develop a fully-automated ACC method for checking BIM-represented building designs for compliance with energy codes and contract specifications. The research included six primary research tasks: (1) conducting a comprehensive literature review; (2) developing a semantic, domain-specific, machine learning-based text classification method and algorithm for classifying energy regulatory documents (including energy codes) and contract specifications for supporting energy ACC in construction; (3) developing a semantic, natural language processing (NLP)-enabled, rule-based information extraction method and algorithm for automated extraction of energy requirements from energy codes; (4) adapting the information extraction method and algorithm for automated extraction of energy requirements from contract specifications; (5) developing a fully-automated, semantic information alignment method and algorithm for aligning the representations used in the BIMs to the representations used in the energy codes and contract specifications; and (6) implementing the aforementioned methods and algorithms in a fully-automated energy compliance checking prototype, called EnergyACC, and using it in conducting a case study to identify the feasibility and challenges for developing an ACC method that is fully-automated and generalized across different types of regulatory documents. Promising noncompliance detection performance was achieved for both energy code checking (95.7% recall and 85.9% precision) and contract specification checking (100% recall and 86.5% precision)

The ISD process as a 'live routine'

The objective of this exploratory study is to view an ISD process as an organisational routine in an effort to identify the inefficiencies in the process. To meet this objective we present the findings from a sixteen month case study of an ISD organisation, examining the ISD process the starting point of which is the requirements elicitation (RE) phase. A variety of data gathering techniques are used across two phases of data collection. The case data is organised as a narrative network (NN) of the organisational routine (ISD process) allowing us to understand the ISD process as a collection of functional events/narrative fragments, generated by the enactment of the organisational routine. The NN is showing itself to be a very powerful device to appreciate the knock-on impacts of vague and incomplete requirements (poor requirements elicitation) on downstream ISD process ’patterns of action’. In the findings of this research we generate an ‘ostensive’ rule that defines a valid sequence of action in the ISD process. As a case study, the methods and results provide a means of comparison to additional cases of ISD organisations

Automated Natural Language Requirements Analysis using General Architecture for Text Engineering (GATE) Framework

Stakeholders exchange ideas and describe requirements of the system in natural language at the early stage of software development. These software requirements tend to be unclear, incomplete and inconsistent. However, better quality and low cost of system development are grounded on clear, complete and consistent requirements statements. Requirements boilerplate is an effective way to minimise the ambiguity from the natural language requirements. But manual conformance of natural language requirements with boilerplate is time consuming and difficult task. This paper aims to automate requirements analysis phase using language processing tool. We propose a natural language requirement analysis model. We also present an open source General Architecture for Text Engineering (GATE) framework for automatically checking of natural language requirements against boilerplates for conformance. The evaluation of proposed approach shows that GATE framework is only capable of detecting ambiguity in natural language requirements. We also present the rules to minimise ambiguity, incompleteness, and inconsistency