Requirements Engineering Tasks Research Articles

Specifying requirements for collection and analysis of online user feedback.

According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.

The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review

Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their linguistic information, and convert them to easy-to-process representations, such as lists of features or embedding-based vector representations. These NLP-based representations are usually used at a later stage as inputs for machine learning techniques or rule-based methods. Thus, requirements representations play a major role in determining the accuracy of different approaches. In this paper, we conducted a survey in the form of a systematic literature mapping (classification) to find out (1) what are the representations used in RE tasks literature, (2) what is the main focus of these works, (3) what are the main research directions in this domain, and (4) what are the gaps and potential future directions. After compiling an initial pool of 2,227 papers, and applying a set of inclusion/exclusion criteria, we obtained a final pool containing 104 relevant papers. Our survey shows that the research direction has changed from the use of lexical and syntactic features to the use of advanced embedding techniques, especially in the last two years. Using advanced embedding representations has proved its effectiveness in most RE tasks (such as requirement analysis, extracting requirements from reviews and forums, and semantic-level quality tasks). However, representations that are based on lexical and syntactic features are still more appropriate for other RE tasks (such as modeling and syntax-level quality tasks) since they provide the required information for the rules and regular expressions used when handling these tasks. In addition, we identify four gaps in the existing literature, why they matter, and how future research can begin to address them.

Exploring Automated GDPR-Compliance in Requirements Engineering: A Systematic Mapping Study

The General Data Protection Regulation (GDPR), adopted in 2018, profoundly impacts information processing organizations as they must comply with this regulation. In this research, we consider GDPR-compliance as a high-level goal in software development that should be addressed at the outset of software development, meaning during requirements engineering (RE). In this work, we hypothesize that natural language processing (NLP) can offer a viable means to automate this process. We conducted a systematic mapping study to explore the existing literature on the intersection of GDPR, NLP, and RE. As a result, we identified 448 relevant studies, of which the majority (420) were related to NLP and RE. Research on the intersection of GDPR and NLP yielded nine studies, while 20 studies were related to GDPR and RE. Even though only one study was identified on the convergence of GDPR, NLP, and RE, the mapping results indicate opportunities for bridging the gap between these fields. In particular, we identified possibilities for introducing NLP techniques to automate manual RE tasks in the crossing of GDPR and RE, in addition to possibilities of using NLP-based machine learning techniques to achieve GDPR-compliance in RE.

WITHDRAWN: A Review of Machine Learning Algorithms for Identification and Classification of Non-Functional Requirements

Abstract Context Recent developments in requirements engineering (RE) methods have seen a surge in using machine-learning (ML) algorithms to solve some difficult RE problems. One such problem is identification and classification of non-functional requirements (NFRs) in requirements documents. ML-based approaches to this problem have shown to produce promising results, better than those produced by traditional natural language processing (NLP) approaches. Yet, a systematic understanding of these ML approaches is still lacking. Method This article reports on a systematic review of 24 ML-based approaches for identifying and classifying NFRs. Directed by three research questions, this article aims to understand what ML algorithms are used in these approaches, how these algorithms work and how they are evaluated. Results (1) 16 different ML algorithms are found in these approaches; of which supervised learning algorithms are most popular. (2) All 24 approaches have followed a standard process in identifying and classifying NFRs. (3) Precision and recall are the most used matrices to measure the performance of these approaches. Finding The review finds that while ML-based approaches have the potential in the classification and identification of NFRs, they face some open challenges that will affect their performance and practical application. Impact The review calls for the close collaboration between RE and ML researchers, to address open challenges facing the development of real-world ML systems. Significance The use of ML in RE opens up exciting opportunities to develop novel expert and intelligent systems to support RE tasks and processes. This implies that RE is being transformed into an application of modern expert systems.

Development of Requirements Pattern Repository: Towards Supporting Requirements Reuse

Requirements Engineering (RE) covers the study of techniques to define users’ needs by generating quality requirements. Nonetheless, RE tasks require a considerable amount of effort and resources due to various issues and challenging aspects. A crucial RE concern is how to effectively perform the RE tasks in order to reduce development cost. Amongst the recommended technique to achieve this, is through Requirements Reuse (RR) and one of the means to attain RR is by using requirements pattern. Reusability, in general can be viewed from for-reuse and with-reuse stages. The use of the requirements patterns could assist requirement engineers to create patterns for future reuse i.e., for-reuse and/or adapt in an environment i.e., with-reuse stage. Recent developments in RR have heightened the need for suitable environment to support these processes. In the current situation, without a requirements pattern repository, requirements engineers need to manually create, store, search, select and adapt the requirements patterns, which suit their needs. However, requirement engineers also need to be properly trained in order to make decision as to which pattern to choose, how the selection can best suit their needs and also what could be saved by using the pattern. Hence, the objectives of this work are to (1) create an environment that supports requirement engineers to create, adopt or adapt requirements patterns for-reuse and with-reuse processes; (2) support for-reuse process by creating requirements pattern repository; and (3) facilitate with-reuse process by allowing the requirements pattern adaptation from the existing patterns in the repository. In this work, we analyzed the literature on how requirements patterns are applied in both the stages and defined the required environment for the requirements pattern repository. In the end, we developed a requirements pattern repository based on generic domain and domain specific requirements patterns based on Collaborative Meeting and Appointment Scheduler Domain to demonstrate the proof-of-concept to allow the repository to be operational.

Requirements Engineering Tasks.

Many managers and others who are not professional requirements engineers tend to greatly over-simplify requirements engineering (RE). Based on their observations that requirements specifications primarily contain narrative English textual statements of individual requirements and that all members of the engineering team are reasonably literate, there is a common myth that practically anyone with little or no specialized training or expertise can be a requirements engineer. After all, what is there to do but ask a few stakeholders what they want (requirements elicitation), study the resulting requirements to make sure they are understood (requirements analysis), write the requirements down in a document (requirements specification), and then ask the customer if they’re right (requirements validation). Just give the team a short class in use case modeling, and they are ready to go. Unfortunately, the preceding is a misleading, if much too prevalent, myth. While these four RE tasks (not sequential phases!) are commonly performed with varying degrees of completeness, rigor, and success on most projects, a list of tasks containing only these four is far from complete. The purpose of this paper is to provide a brief