Improving requirements with NLP techniques

Elaborating “good” requirements specifications is an important factor for the success of a software project. Requirements are normally expressed using textual descriptions in natural language, but not without problems. Some requirements documentation techniques, such as use cases specifications, often focus on functionality and leave many concerns understated in the text and scattered through several documents. These concerns, commonly known as crosscutting or architecturally-relevant concerns, often come from business goals or quality attributes that must be clearly identified by analysts and developers, as these concerns can have a far-reaching effect in the development process. Not treating these concerns at early development stages can lead to poor design solutions that become difficult (and costly) to fix afterwards. Unfortunately, searching for concerns in textual requirements is a difficult and time-consuming task for analysts, because requirements are often poorly modularized and there is text duplicated across documents. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Improving requirements with NLP techniques

Elaborating “good” requirements specifications is an important factor for the success of a software project. Requirements are normally expressed using textual descriptions in natural language, but not without problems. Some requirements documentation techniques, such as use cases specifications, often focus on functionality and leave many concerns understated in the text and scattered through several documents. These concerns, commonly known as crosscutting or architecturally-relevant concerns, often come from business goals or quality attributes that must be clearly identified by analysts and developers, as these concerns can have a far-reaching effect in the development process. Not treating these concerns at early development stages can lead to poor design solutions that become difficult (and costly) to fix afterwards. Unfortunately, searching for concerns in textual requirements is a difficult and time-consuming task for analysts, because requirements are often poorly modularized and there is text duplicated across documents. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

An Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous, i.e., not relevant to the domain model. Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements, and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance

SCRAM-CK: applying a collaborative requirements engineering process for designing a web based e-science toolkit

This paper presents SCRAM–CK, a method to elicit requirements by means of strong user involvement supported by prototyping activities. The method integrates two existing approaches, SCRAM and CK theory. SCRAM provides the framework for requirements management, while CK theory provides a framework for reasoning about design and its evolution. The method is demonstrated with the definition and refining of requirements for the BioVeL web toolkit. The objective of BioVeL is to allow scientists to understand, run, modify and construct workflows for data analysis with minimal training using a web-based interface. The proposed method is supported by prototyping activities for gathering user feedback, and refining requirements and design proposals. Using this method, the prototypes evolved from simple workflow execution enablers to include more complex functionalities for reviewing, modifying and building workflows in later versions. This paper presents a contribution to the application of techniques for requirements engineering. SCRAM–CK is an amalgamated method that combines a user-centred continuous refinement approach with support for design evolution through prototyping. The paper also shows the influence of the requirements engineering process in the evolution of design proposals