Requirements ranking based on crowd-sourcing high-end product USs

doi:10.23919/JSEE.2023.000164

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (1): 94-104.doi: 10.23919/JSEE.2023.000164

• SYSTEMS ENGINEERING • Previous Articles

Requirements ranking based on crowd-sourcing high-end product USs

Yufeng MA(), Yajie DOU(), Xiangqian XU(), Qingyang JIA(), Yuejin TAN()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410003, China

Received:2021-12-20 Online:2024-02-18 Published:2024-03-05
Contact: Yajie DOU E-mail:mashuang9707@163.com;yajiedou_nudt@163.com;xuxiangqian18@163.com;cassie_qing@163.com;yjtan@nudt.edu.cm
About author:
MA Yufeng was born in 1997. She received her B.E. degree in industrial engineering from Northeast Forestry University, Harbin, China, in 2020. She is currently pursuing her Ph.D. degree in management science and engineering in National University of Defense Technology. Her research interests include intelligent requirements engineering, knowledge graph, knowledge representation and reasoning, and knowledge embedding. E-mail: mashuang9707@163.com

DOU Yajie was born in 1987. He received his Ph.D. degree in management science and engineering from National University of Defense Technology, Changsha, China, in 2016. He is currently an associate professor of College of Systems Engineering, National University of Defense Technology. His research interests include system- of-systems architecture and engineering management, intelligent requirements engineering and complex project management. E-mail: yajiedou_nudt@163.com

XU Xiangqian was born in 1995. He received his B.E. degree in systems engineering from National University of Defense Technology, Changsha, China, in 2018. He is currently pursuing his Ph.D. degree in management science and engineering in National University of Defense Technology. His research interests include intelligent requirements engineering, complex project management, defense acquisition, and system-of-systems requirement modeling. E-mail: xuxiangqian18@163.com

JIA Qingyang was born in 1998. She received her B.M. degree in financial engineering from Hunan University, Changsha, China, in 2020. She is currently pursuing her Ph.D. degree in management science and engineering in National University of Defense Technology. Her research interests is portfolio selection decision. E-mail: cassie_qing@163.com

TAN Yuejin was born in 1958. He received his M.E. degree in systems engineering from National University of Defense Technology, Changsha, China, in 1985. He is currently a professor of college of systems engineering, National University of Defense Technology. His research interests include system-of-systems architecture and engineering management, intelligent requirements engineering, complex project management, defense acquisition, and system-of-systems requirement modeling. E-mail: yjtan@nudt.edu.cm
Supported by:
This work was supported by the National Natural Science Foundation of China (71690233; 71901214).

Abstract

Abstract:

Based on the characteristics of high-end products, crowd-sourcing user stories can be seen as an effective means of gathering requirements, involving a large user base and generating a substantial amount of unstructured feedback. The key challenge lies in transforming abstract user needs into specific ones, requiring integration and analysis. Therefore, we propose a topic mining-based approach to categorize, summarize, and rank product requirements from user stories. Specifically, after determining the number of story categories based on pyLDAvis, we initially classify “I want to” phrases within user stories. Subsequently, classic topic models are applied to each category to generate their names, defining each post-classification user story category as a requirement. Furthermore, a weighted ranking function is devised to calculate the importance of each requirement. Finally, we validate the effectiveness and feasibility of the proposed method using 2 966 crowd-sourced user stories related to smart home systems.

Key words: high-end product, complex system, crowd-sourcing user stories, topic mining, requirements ranking

Yufeng MA, Yajie DOU, Xiangqian XU, Qingyang JIA, Yuejin TAN. Requirements ranking based on crowd-sourcing high-end product USs[J]. Journal of Systems Engineering and Electronics, 2024, 35(1): 94-104.

Figures/Tables 9

Table 1

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Table 2

Table 3

References 31

1	WANG X F, WANG T,ZHOU X, et al. Research on strategic risk identification method of equipment system development based on system dynamics[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1225−1234.
2	PENG J G,XIA G, SUN B Q, et al A multi-enterprise quality function deployment paradigm with unstructured decision-making in linguistic contexts. Journal of Systems Engineering and Electronics, 2023, 34 (4): 966- 980.
3	SCHON E M, THOMASCHEWSKI J, ESCALONA M J. Agile requirements engineering: a systematic literature review. Computer Standards and Interfaces, 2017, 49: 79−91.
4	INAYAT I, SALIM S S, MARCZAK S, et al A systematic literature review on agile requirements engineering practices and challenges. Computers in Human Behavior, 2015, 51 (10): 915- 929.
5	RAHARJANA I K, SIAHAAN D, FATICHAH C User stories and natural language processing: a systematic literature review. IEEE Access, 2021, 9, 53811- 53826. doi: 10.1109/ACCESS.2021.3070606
6	EBERT C, RAY R Test-driven requirements engineering. IEEE Software, 2020, 38 (1): 16- 24.
7	GREGORY S Requirements for the new normal: requirements engineering in a pandemic. IEEE Software, 2021, 38 (2): 15- 18. doi: 10.1109/MS.2020.3044403
8	DE GEA J M C, EBERT C, HOSNI M, et al Requirements engineering tools: an evaluation. IEEE Software, 2021, 38 (3): 17- 24. doi: 10.1109/MS.2021.3058394
9	DALPIAZ F, COOPER K M L Games for requirements engineers: analysis and directions. IEEE Software, 2018, 37 (1): 50- 59.
10	GUZMAN E, MAALEJ W. How do users like this feature? A fine grained sentiment analysis of app reviews. Proc. of the IEEE 22nd International Requirements Engineering Conference, 2014: 153−162.
11	BEHUTIYE W, KARHAP P, LOPEZ L, et al Management of quality requirements in agile and rapid software development: a systematic mapping study. Information and Software Technology, 2019, 123, 106225.
12	TIWARI S, GUPTA A A systematic literature review of use case specifications research. Information and Software Technology, 2015, 67 (2): 128- 158.
13	LONIEWSKI G, EMILIO I, SILVIA A. A systematic review of the use of requirements engineering techniques in model-driven development. Proc. of the 13th International Conference on Model Driven Engineering Languages & Systems, 2010: 213−227.
14	AGRAWAL A, FU W, MENZIES T What is wrong with topic modeling? And how to fix it using search-based software engineering. Information & Software Technology, 2018, 98, 74- 88.
15	CARRENO L V G, WINBLADH K. Analysis of user comments: an approach for software requirements evolution. Proc. of the 35th International Conference on Software Engineering, 2013: 582−591.
16	CHEN N, LIN J L, HOI S C H, et al. AR-miner: mining informative reviews for developers from mobile app marketplace. Proc. of the 36th International Conference on Software Engineering, 2014: 767−788.
17	ASUNCION H U, ASUNCION A U, TAYLOR R N. Software traceability with topic modeling. Proc. of the ACM/IEEE International Conference on Software Engineering, 2010: 95–104.
18	BARUA A, THOMAS S W, HASSAN A E What are developers talking about? An analysis of topics and trends in stack overflow. Empirical Software Engineering, 2014, 19 (3): 619- 654. doi: 10.1007/s10664-012-9231-y
19	TONG Z, ZHANG H Y. A text mining research based on LDA topic modelling. Proc. of the 6th International Conference on Computer Science, Engineering and Information Technology, 2016: 21–22.
20	HINDLE A, BIRD C, ZIMMERMANN T, et al. Relating requirements to implementation via topic analysis: do topics extracted from requirements make sense to managers and developers? Proc. of the 28th IEEE International Conference on Software Maintenance, 2012: 243–252.
21	LPEZ M J, RAMREZ N A, JUAREZ R R , et al. Analysis of planning poker factors between university and enterprise. Proc. of the International Conference in Software Engineering Research and Innovation, 2017: 54−60.
22	SCOTT E, PFAHL D. Using developers’ features to estimate story points. Proc. of the International Conference on Software and System Process, 2018: 106−110.
23	DURAN M, JUAREZ-RAMIREZ R, JIMENEZ S, et al User story estimation based on the complexity decomposition using bayesian networks. Programming and Computer Software, 2020, 46 (8): 569- 583. doi: 10.1134/S0361768820080095
24	ABDOU S M, YONG K, OTHMAN M. Categorization of project complexity factors in Malaysian construction industry. International Journal of Property Sciences, 2017, 7(1): 1−15. DOI: https://doi.org/10.22452/ijps.vol7nol.3.
25	BOSCH-REKVELDT M, BAKKER H, HERTOGH M. Comparing project complexity across different industry sectors. Complexity, 2018, 2018: 3246508.
26	DALPIAZ F, IVOR V, BRINKKEMPER S, et al Detecting terminological ambiguity in USs: tool and experimentation. Information and Software Technology, 2019, 110 (6): 3- 16.
27	BANO M. Addressing the challenges of requirements ambiguity: a review of empirical literature. Proc. of the International Workshop on Empirical Requirements Engineering, 2015: 21−24.
28	ROSADINI B, FERRARI A, GORI G, et al. Using NLP to detect requirements defects: an industrial experience in the railway domain. Proc. of the International Working Conference on Requirements Engineering: Foundation for Software Quality, 2017: 344−360.
29	PAUL S, PADHY N P Real-time energy management for smart homes. IEEE Systems Journal, 2020, 15 (3): 4177- 4188.
30	MURUKANNAIAH P K, AJMERI N, SINGH M P. Toward automating crowd RE. Proc. of the IEEE International Requirements Engineering Conference, 2017: 512−515.
31	MURUKANNAIAH P K, AJMERI N, SINGH M P. Acquiring creative requirements from the crowd: understanding the influences of personality and creative potential in crowd RE. Proc. of the IEEE International Requirements Engineering Conference, 2016: 176−185.

Reference	Technique	Goal
[5]	Sentiment analysis, LDA	Extract software features from user reviews and summarize each feature to obtain higher dimensional features
[15]	Sentiment analysis, LDA	Extract word-based topics from reviews and assign sentiments to them
[16]	LDA, sentiment unification model, heuristics	Summarize and rank the informative reviews
[17]	Record traceability links, semantic categorization, topic visualization	Aid users to analyze the semantic nature of artifacts and the software architecture itself
[18]	LDA	Identify the main topics in the textual content of stack overflow discussions
[19]	Evaluate 200 000 Wikipedia articles, apply LDA-based approach to twitter messages from 10 000 users	Retrieve articles as well as twitter users that cover similar content
[20]	LDA	Extract topics from documented requirements at Microsoft

Identity	Name of requirements	Name of clusters
R1-1	Outside temperature detecting	Temperature control
R1-2	Heat temperature detect by sensor
R1-3	Food temperature reminding
R1-4	Temperature detector control
R2-1	Save water	Save energy
R2-2	Light control at night
R2-3	Thermostat control
R2-4	Window control
R3-1	TV show recommend	Entertainment device
R3-2	Open game system
R3-3	Bathroom atmosphere
R3-4	Remote control
R4-1	Dangerous alarm by voice	Dangerous alarm
R4-2	Device lock alarm
R4-3	Window and door alarm
R4-4	Device running time abnormal alarm
R5-1	App control house	Human-Machine Interaction
R5-2	Monitor shows information
R5-3	Device open by voice
R5-4	Temperature control by voice
R6-1	Safety alarm by TV	Monitor alarm
R6-2	Lock detecting
R6-3	Kitchen device alarm
R7-1	Hazardous substance detection	Health
R7-2	Pet health management
R7-3	Children’s activity alarm
R7-4	Diet plan

Identity	$ {s_j} $	$ {a_j} $	$ {m_c} $
R1-1	0.242	0.046	0.156
R1-2	0.348	0.052
R1-3	0.527	0.056
R1-4	0.167	0.051
R2-1	0.133	0.035	0.124
R2-2	0.198	0.058
R2-3	0.143	0.059
R2-4	0.515	0.033
R3-1	0.200	0.022	0.078
R3-2	0.176	0.023
R3-3	0.115	0.026
R3-4	0.164	0.023
R4-1	0.156	0.022	0.107
R4-2	0.290	0.104
R4-3	0.104	0.042
R4-4	0.228	0.034
R5-1	0.116	0.014	0.059
R5-2	0.085	0.024
R5-3	0.243	0.012
R5-4	0.060	0.017
R6-1	0.089	0.053	0.096
R6-2	0.338	0.054
R6-3	0.028	0.073
R7-1	0.404	0.018	0.105
R7-2	0.214	0.019
R7-3	0.243	0.012
R7-4	0.105	0.019

[1]	Kewei YANG, Jichao LI, Maidi LIU, Tianyang LEI, Xueming XU, Hongqian WU, Jiaping CAO, Gaoxin QI. Complex systems and network science: a survey [J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 543-573.
[2]	Zhenya WU, Jianping HAO. Clustering-based maintainability demonstration for complex systems with a mixed maintenance time distribution [J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1260-1271.

Requirements ranking based on crowd-sourcing high-end product USs

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 9

References 31

Related Articles 2

Recommended Articles

Metrics

Comments