The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea CO2flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in-situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in-situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in-situ measurements. To address this, we propose a novel Multi-Parametric Regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in-situ data from the Global Ocean Data Analysis Project (GLODAP), trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in-situ measurements. Additionally, seasonal and inter-annual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.

SRCM: A semi formal requirements representation model enabling system visualisation and quality checking

Most existing automated requirements formalisation techniques require system engineers to (re)write their requirements using a set of predefined requirement templates with a fixed structure and known semantics to simplify the formalisation process. However, these techniques require understanding and memorising requirement templates, which are usually fixed format, limit requirements captured, and do not allow capture of more diverse requirements. To address these limitations, we need a reference model that captures key requirement details regardless of their structure, format or order. Then, using NLP techniques we can transform textual requirements into the reference model. Finally, using a suite of transformation rules we can then convert these requirements into formal notations. In this paper, we introduce the first and key step in this process, a Requirement Capturing Model (RCM) - as a reference model - to model the key elements of a system requirement regardless of their format, or order. We evaluated the robustness of the RCM model compared to 15 existing requirements representation approaches and a benchmark of 162 requirements. Our evaluation shows that RCM breakdowns support a wider range of requirements formats compared to the existing approaches. We also implemented a suite of transformation rules that transforms RCM-based requirements into temporal logic(s). In the future, we will develop NLP-based RCM extraction technique to provide end-to-end solution.

RCM-Extractor: Automated Extraction of a Semi Formal Representation Model from Natural Language Requirements

Requirements quality checking is a key process in requirements engineering. For complex and large scale systems, it is recommended to use automated requirements quality checking tools because of the size and complexity of requirements. However, such tools are typically evaluated on a small set of manually curated requirements. This limitation affects the comprehensiveness and reliability of the evaluation and leaves several possible quality issues undetected. In this paper, we de-scribe a novel quality-checking-oriented synthesised requirements generator. We provide an input description language so that several quality checking issues and scenarios can be defined. The generator utilises an input dictionary of nouns and verb frames, and generates requirements sentences complying to a user-defined description of a quality affected requirement.

The formalisation of natural language (NL) requirements is a challenging problem because NL is inherently vague and imprecise. Existing formalisation approaches only support requirements adhering to specific boilerplates or templates, and are affected by the requirements quality issues. Several quality models are developed to assess the quality of NL requirements. However, they do not focus on the quality issues affecting the formalisability of requirements. Such issues can greatly compromise the operation of complex systems and even lead to catastrophic consequences or loss of life (in case of critical systems). In this paper, we propose a requirements quality checking approach utilising natural language processing (NLP) analysis. The approach assesses the quality of the requirements against a quality model that we developed to enhance the formalisability of NL requirements. We evaluate the effectiveness of our approach by comparing the formalisation efficiency of a recent automatic formalisation technique before and after utilising our approach. The results show an increase of approximately 15% in the F-measure (from 83.8% to 98%).

Formal verification techniques enable the detection of complex quality issues within system specifications. However, the majority of system requirements are usually specified in natural language (NL). Manual formalisation of NL requirements is an error-prone and labour-intensive process requiring strong mathematical expertise, and can be infeasible for large numbers of requirements. Existing automatic formalisation techniques usually support heavily constrained natural language relying on requirement boilerplates or templates. In this paper, we introduce ARF: Automatic Requirements Formalisation Tool. ARF can automatically transform free-format natural language requirements into temporal logic based formal notations. This is achieved through two steps: 1) extraction of key requirement attributes into an intermediate representation (RCM: Requirement Capturing Model), and 2) transformation rules that convert requirements from the RCM format to formal notations.

It is pivotal to have well-specified requirements to eliminate errors at an early stage of the system development life cycle.Some quality standards recommend the use of formal methods -mandate requirements to be expressed in formal notations -to detect errors.However, formal notations are not suitable for non-experts and may not be understood by all the stakeholder.To fix this, bidirectional transformations among requirement representation levels are required to maintain traceability and facilitate the communication of requirements among all the involved parties.This paper reflects on the different formality levels of requirements specifications including: informal, semi-formal, and formal notations.In addition, an automated multi-layer transformation approach is proposed to enable bi-directional transformation among requirements levels.

The quality of a delivered product relies heavily upon the quality of its requirements. Across many disciplines and domains, system and software requirements are mostly specified in natural language (NL). However, natural language is inherently ambiguous and inconsistent. Such intrinsic challenges can lead to misinterpretations and errors that propagate to the subsequent phases of the system development. Pattern-based natural language processing (NLP) techniques have been proposed to detect the ambiguity in requirements specifications. However, such approaches typically address specific cases or patterns and lack the versatility essential to detecting different cases and forms of ambiguity. In this paper, we propose an efficient and versatile automatic syntactic ambiguity detection technique for NL requirements. The proposed technique relies on filtering the possible scored interpretations of a given sentence obtained via Stanford CoreNLP library. In addition, it provides feedback to the user with the possible correct interpretations to resolve the ambiguity. Our approach incorporates four filtering pipelines on the input NL-requirements working in conjunction with the CoreNLP library to provide the most likely possible correct interpretations of a requirement. We evaluated our approach on a suite of datasets of 126 requirements and achieved 65% precision and 99% recall on average.