Requirements engineering challenges and practices in large-scale agile system development

Kasauli, Rashidah; Knauss, Eric; Horkoff, Jennifer; Liebel, Grischa; Neto, Francisco Gomes de Oliveira

doi:10.1016/j.jss.2020.110851

Cited by 78 publications

(37 citation statements)

References 18 publications

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“…2); in our previous study [5], we focused on the agile RE approach, as well as in [36]. However, we have received feedback regarding this focus on agile RE suggesting that the metamodel and the process could instead be proposed in a way that supports both agile and conventional RE methodologies; this is also supported by empirical studies according to which the two approaches are often mixed in practice, especially in large-scale agile development [11,12]. We have therefore in this study generalized the part of the metamodel related to requirements.…”

Section: Discussionmentioning

confidence: 89%

“…Even though the two approaches differ in terms of working practices and roles, as well as in the templates used for requirements, they share some common aims, such as the need for continuous [10] and extensive requirements elicitation (which in plan-based approaches is done more upfront; in agile as late as possible), classification of requirements types, and prioritization. Recent studies have, in fact, reported the empirical perspective that the two approaches are often mixed in practice, especially in large-scale agile development [11] and that it is possible to combine them [12].…”

Section: Requirements Elicitationmentioning

confidence: 99%

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Holistic data-driven requirements elicitation in the big data era

Henriksson

Zdravković

2021

Softw Syst Model

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Digital transformation stimulates continuous generation of large amounts of digital data, both in organizations and in society at large. As a consequence, there have been growing efforts in the Requirements Engineering community to consider digital data as sources for requirements acquisition, in addition to human stakeholders. The volume, velocity and variety of the data make requirements discovery increasingly dynamic, but also unstructured and complex, which current elicitation methods are unable to consider and manage in a systematic and efficient manner. We propose a framework, in the form of a conceptual metamodel and a method, for continuous and automated acquisition, analysis and aggregation of heterogeneous digital sources that aims to support data-driven requirements elicitation and management. The usability of the framework is partially validated by an in-depth case study from the business sector of video game development.

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Section: Discussionmentioning

confidence: 89%

Section: Requirements Elicitationmentioning

confidence: 99%

Holistic data-driven requirements elicitation in the big data era

Henriksson

Zdravković

2021

Softw Syst Model

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“…Several empirical studies have discussed requirement engineering challenges in the software development life cycle [8]. These challenges have been traced and collected under two main aspects, which are customer and system aspects, as shown in Fig.…”

Section: A Requirements Elicitationmentioning

confidence: 99%

“…Additionally, storing requirements in the form of user stories in a system backlog is not an appropriate solution to fully understand the system requirement and track updates on it according to the agile development life cycle [10]. Prioritizing requirements according to specifics factors such as business value, risk, importance, cost, dependency, and constraints becomes more challenging in a complex system [8]. The above challenges significantly affect requirement quality, especially when dealing with complex projects such as big data projects, as we have discussed in this study.…”

Section: A Requirements Elicitationmentioning

confidence: 99%

Enhanced Framework for Big Data Requirement Elicitation

Hesham¹,

Emam²,

Salah³

2021

IJACSA

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Requirement engineering is one of the software development life cycle phases; it has been recognized as an important phase for collecting and analyzing a system's goals. However, despite its importance, requirement engineering has several limitations such as incomplete requirements, vague requirements, lack of prioritization, and less user involvement, all of which affect requirement quality. With the emergence of big data technology, the complexity of big data, which is defined by large data volume, high velocity, and large data variety, has gradually increased, affecting the quality of big data software requirements. This study proposes a framework with four sequential phases to improve requirement engineering quality through big data software development. By integrating the proposed framework's phases in which user requirements are collected in a complete vision using traditional requirement elicitation techniques with agile methodology and mind mapping, the collected requirements are displayed via a graphical representation using mind maps to achieve high requirement accuracy with connectivity and modifiability, enabling the accurate prioritization of requirements implemented using agile SCRUM methodology. The proposed framework improves requirement quality in big data software development, which is represented by accuracy, completeness, connectivity, and modifiability to understand the value of the collected requirements and effectively affect the quality of the implementation phase.

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“…In another study of GSD projects, RE problems in multi-site software development organisations were identified [13]. Requirements in many projects (GSD or not) are often constantly evolving and new requirements emerging [14]. Traditional on-site software development (in non-GSD projects) have always benefited from continuous collaboration between clients and developers [15].…”

Section: Introductionmentioning

confidence: 99%

Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development

et al. 2021

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There is a need to identify requirements engineering (RE) practices that are important to global software development (GSD) project success. The objective of this paper is to report our recent empirical study results which aimed to identify the RE practices that are important to GSD projects. This study used an online survey questionnaire to elicit data from 56 RE experts of GSD projects. The survey included 66 RE practices identified by Sommerville et al. [1] for non-GSD projects. The participants were asked to rank each RE practice on a four-point scale to determine the degree of importance of each practice in the context of GSD projects. This research identified a set of six key RE practices that mainly focuses on GSD project stakeholders, scope, standards and requirements traceability management. One common theme that is evident from the RE experts' feedback analysis is the standardization of requirements documents to reduce requirements inconsistencies and improve communication in diverse and distributed GSD project environments Our results show that not all 66 RE best practices are important for GSD projects. We believe that a good understanding of the identified RE practices is vital in developing and implementing the situation-specific RE processes for GSD projects.

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Requirements engineering challenges and practices in large-scale agile system development

Cited by 78 publications

References 18 publications

Holistic data-driven requirements elicitation in the big data era

Holistic data-driven requirements elicitation in the big data era

Enhanced Framework for Big Data Requirement Elicitation

Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development

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