SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

Hujainah, Fadhl; Bakar, Rohani Abu; Nasser, Abdullah B.; Al-haimi, Basheer; Zamli, Kamal Z.

doi:10.1016/j.infsof.2020.106501

Cited by 12 publications

(23 citation statements)

References 25 publications

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“…We also used different names for the algorithms that come under the umbrella of artificial intelligence. Then we identified keywords from the relevant research articles [32], [38], [57], [62], [58], [64], [63], [65]. Finally, we used Boolean OR and AND to create the search strings by combining key terms.…”

Section: ) Search Stringsmentioning

confidence: 99%

“…The chosen model forecasts the probability of completing tasks using gradient decision tree-boosting with a logistic output. 9) Hujainah, Bakar, Nasser, Al-Haimi, and Zamli (2021) Hujainah et al in [65] propose a new semi-automated RP technique (SRPTackle) and automation implementation tool (SRPTackle-Tool) to address problems with existing RP techniques such as time consumption, scalability, excessive reliance on expert intervention, automation and the lack of a SQP process for evaluating stakeholder impact in prioritizing requirements. The proposed SRPTackle provides a semi-automated process for prioritizing a large number of requirements without requiring manual intervention, as well as reducing the need for expert intervention in assigning priority values to requirements, classifying requirements, running the SQP process and generating a ranked list of requirements.…”

Section: ) Gupta and Gupta (2018)mentioning

confidence: 99%

“…All of the surveyed machine learning techniques proposed in [5], [8], [19], [26], [52], [54], [34], [65], and [58] offer scalable solutions, as they can prioritize a large number of software requirements and can be used in a variety of scenarios. Table XXX shows that all techniques can be easily extended and overcome redundancy problems by removing repeating groups of requirements, which leads to efficiency in terms of cost and time.…”

Section: ) Strengths and Weaknesses Of Machine Learning-based Techniq...mentioning

confidence: 99%

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A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Anwar,

Bashir

2023

IEEE Access

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Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved with the inclusion of artificial intelligence algorithms and strategies. For this several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.

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Section: ) Search Stringsmentioning

confidence: 99%

Section: ) Gupta and Gupta (2018)mentioning

confidence: 99%

Section: ) Strengths and Weaknesses Of Machine Learning-based Techniq...mentioning

confidence: 99%

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A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Anwar,

Bashir

2023

IEEE Access

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“… Hujainah et al (2021) proposed a technique for prioritizing requirements and thus selecting the requirements to be developed. While not directly recommending, prioritization helps the selection of requirements and supports the process.…”

Section: Related Workmentioning

confidence: 99%

Towards ubiquitous requirements engineering through recommendations based on context histories

Lima

Filippetto

Heckler

et al. 2022

PeerJ Computer Science

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The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations.

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“…These elements are criteria for requirements, stakeholders, procedures, and implementation, which play important roles in prioritizing requirements. To incorporate the interrelated elements for prioritization, further research [11] proposed a semi-automated scalable prioritization technique to improve the prioritization efficiency for a large-scale project.…”

Section: Gilb and Maiermentioning

confidence: 99%

MCBRank Method to Improve Software Requirements Prioritization

Ahmad¹,

Rizawanti²,

Woodings³

et al. 2022

IJACSA

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Software Requirements Prioritization is an important issue that has a more profound effect on the overall quality of software development. Application of software requirements prioritization provides benefits to minimize risks in software development so that the most important and most impactful requirements are given priority. This paper presents a proposed software requirements prioritization method named MCBRank, which incorporates renowned MoSCoW Method and Case-Based Ranking to improve prioritization correctness. It elaborates on the implementation of MCBRank in an empirical investigation to determine software requirements prioritization for a potential e-library system. The investigation allows the software requirements prioritization process to be implemented by using the proposed MCBRank method. A role-playing empirical investigation with 30 respondents prioritized 31 software requirements, and the results were measured by Cohen Kappa. The kappa results show that MCBRank achieves a better agreement towards the Gold Standard with kappa value of 0.60. Therefore, the investigation results support that MCBRank improves the importance of ranking correctness, representing the stakeholders' wants and the organization's actual needs for the potential e-library system.

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SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

Cited by 12 publications

References 25 publications

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Towards ubiquitous requirements engineering through recommendations based on context histories

MCBRank Method to Improve Software Requirements Prioritization

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