Overcoming Scalability Issues in Analytic Hierarchy Process with ReDCCahp: An Empirical Investigation

Ibriwesh, Iyas; Ho, Sin Ban; Chai, Ian

doi:10.1007/s13369-018-3283-2

Cited by 10 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AHP works only on ones in the Critical group. One of the main limitations of this technique is that it works well only if at least 80 % of all requirements are critical (because users can't know their priorities until completing the prioritization process) [18], [19]. Other limitations [2], [3] are that it does not do consistency checking for the results, and it has not been evaluated on large data sets [17].…”

Section: Related Workmentioning

confidence: 99%

“…Researchers [19] introduced another technique based on AHP, namely, ReDCCahp. The main idea of ReDCCahp is to put every pair of adjunct requirements from the requirements list in one group and make the pairwise comparison among these groups to reduce the number of pairwise comparisons and matrix size.…”

Section: Related Workmentioning

confidence: 99%

“…So it is easy to use and understand by users and more effective than AHP when dealing with a medium number of requirements. But this technique is not highly accurate as it randomly groups the adjacent requirements in the list, which means it does not have a specific requirements grouping method, which is considered its main limitation [19]. It also can deal with only small and medium requirements numbers [13] and doesn't consider the requirements dependencies.…”

Section: Related Workmentioning

confidence: 99%

“…To sort the requirements' scores list, a hybrid algorithm of insertion sort algorithm [19] and merge sort algorithm [32] is applied. The main idea of the hybrid algorithm is to divide the list of requirements scores into chunks.…”

Section: B Scoring and Sorting The Requirementsmentioning

confidence: 99%

See 3 more Smart Citations

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

Mohamed¹,

Mazen²,

Helmy³

2022

IJACSA

View full text Add to dashboard Cite

One of the main activities of software requirements analysis is requirements prioritization. The wrong requirements prioritization is risky as it leads to many software failures. The current requirements prioritization techniques can't deal with large requirement numbers efficiently, which is considered one of their main issues. Many researchers have agreed that the analytical hierarchy process (AHP) is one of the best prioritization techniques as it produces highly accurate results. AHP has two main problems: scalability and inconsistency. These problems have motivated us to propose an improved version of AHP for software requirements prioritization, namely Enhanced AHP (E-AHP). A performance evaluation has been done for the conventional AHP, E-AHP, and one of the recent algorithms that also try to solve the AHP scalability problems, namely removing eigenvalues and introducing the dynamic consistency checking algorithm into AHP (ReDCCahp) algorithmsThe evaluation shows which algorithm takes the least time, uses the least memory, produces the most consistent and accurate results, and has the highest scalability. The three algorithms have been evaluated by running their codes using different numbers of requirements ranging from 10 to 500. The results show that E-AHP is more scalable, takes the least time, uses the least memory, and produces the most consistent and accurate results compared to the other two algorithms. That becomes remarkable when the number of requirements increases. Therefore, E-AHP is suitable to be applied in large software projects, as it can deal efficiently with the large software requirements numbers.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Scoring and Sorting The Requirementsmentioning

confidence: 99%

See 2 more Smart Citations

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

Mohamed¹,

Mazen²,

Helmy³

2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…It means that the pairwise comparison is a time-consuming process, and gets exponentially more complicated as the number of criteria increases (Ribeiro et al, 2011). A subset of MCDM approaches, such as AHP and TOPSIS, are not scalable (Ibriwesh et al, 2018;Khari & Kumar, 2013), so in modifying the list of alternatives or criteria, the whole process of evaluation should be redone. Therefore, these methods are costly to maintain, inflexible to change, and applicable to only a small number of criteria and alternatives (see Tables 2.1 , 3.1, 4.1, 5.6, 6.6, and 7.2).…”

Section: Strengths and Liabilities Of Mcdm Techniquesmentioning

confidence: 99%

Multi-Criteria Decision-Making in Software Production

Farshidi¹

View full text Add to dashboard Cite

Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not have been possible to do without the support and guidance that I received from many people. The entire list of people who encouraged me during my Ph.D. is prolonged, whether within the research itself, forming a suitable environment, or even giving personal support. Probably, I will have forgotten to recall all names.First and foremost, I would like to thank Slinger Jansen. To me, Slinger was more than a supervisor. He was an inspiring and enthusiastic co-promoter who showed me the right course of action in all situations. His encouragements and optimism were indispensable to keep me motivated. Slinger taught me how to do research and then report findings correctly to scientific communities.I would like to express my sincere gratitude to Sjaak Brinkkemper. He actively supported my research and encouraged me to write down high-quality scientific papers. With a few questions, he always helped me to focus on contributions and structure my findings. Most of his remarks will be with me for my entire career as a researcher.I would like to say a special thank you to Rolf de Jong. His support, guidance, and comprehensive insights into the software engineering field have made an inspiring experience for me. And special thanks to Michiel Overeem, whose support as an experienced software engineer and an eager researcher allowed my studies to go the extra mile (sorry for all the additional work, Michiel!).Doing research and writing papers in collaboration with others was a pleasant experience as it allowed me to gain more insight and knowledge regarding what I was working on. I am profoundly grateful for my co-authors' hard work, who have not been mentioned above, and their substantial contribution to uplift the studies presented in this dissertation. Thanks, Sergio España, Jan Martijn van der Werf, Rolf de Jong, and Jacco Verkleij. I am also thankful to my motivated students who assisted me in the research projects, especially Elena Baninemeh, Andrey Krupskiy, Baharak Bakhtiari, and Mahdi Deldar. I am delighted to have worked with you, and I look forward to working with you again.My research would have been impossible without the feedback from domain experts and case study participants from various software producing organizations that participated in the research projects' evaluation phases in this dissertation.The work presented in this dissertation has been critically assessed and approved by an outstanding reading committee to whom I am more than grateful: Patricia Lago, Krzysztof Wnuk, Gabriele Keller, Bedir Tekinerdogan, and Diomidis Spinellis. I also vi Acknowledgments want to thank the numerous anonymous reviewers of the journals and conferences. Often I received constructive feedback that helped me to improve the quality of my publications.To conclude, I cannot forget to thank my parents, beloved sister, and friends for all the unconditional love and support during my Ph.D. journey.

show abstract

Assessing Python Programming Through Personalised Learning Styles Model

Teh

Chai

et al. 2021

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Overcoming Scalability Issues in Analytic Hierarchy Process with ReDCCahp: An Empirical Investigation

Cited by 10 publications

References 33 publications

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

Multi-Criteria Decision-Making in Software Production

Assessing Python Programming Through Personalised Learning Styles Model

Contact Info

Product

Resources

About