Abstract
Automatically identifying quality attributes (e.g., security, performance) in agile user stories could help architects reason about early architecture design decisions before analyzing a product backlog in detail (e.g., through a manual review of stories). For example, architects may already get the “bigger picture” of potential architectural key drivers and constraints. Applying a previously developed method to automatically identify quality attributes in user stories, in this paper we investigate (a) what quality attributes are potentially missed in an automatic analysis of a backlog, and (b) how the importance of quality attributes (based on the frequency of their occurrence in a backlog) differs to that of quality attributes identified in a manual review of a backlog. As in previous works, we analyzed the backlogs of 22 publicly available projects including 1,675 stories. For most backlogs, automatically identified quality attributes are a subset of quality attributes identified manually. On the other hand, the automatic identification would usually not find more (and therefore potentially irrelevant) quality attributes than a manual review. We also found that the ranking of quality attributes differs between the automatically and manually analyzed user stories, but the overall trend of rankings is consistent. Our findings indicate that automatically identifying quality attributes can reduce the effort of an initial backlog analysis, but still provide useful (even though high-level and therefore potentially incomplete) information about quality attributes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We acknowledge that quality attributes are not the only factors with architectural significance; however, other factors are outside the scope of this work.
- 2.
References
Bass, L., Clements, P., Kazman, R.: Software Architecture in Practice. Addison-Wesley, Boston (2012)
Bellomo, S., Gorton, I., Kazman, R.: Toward agile architecture: Insights from 15 years of ATAM. IEEE Softw. 32(5), 38–45 (2015)
Bhat, M., Shumaiev, K., Biesdorf, A., Hohenstein, U., Matthes, F.: Automatic extraction of design decisions from issue management systems: a machine learning based approach. In: Lopes, A., de Lemos, R. (eds.) ECSA 2017. LNCS, vol. 10475, pp. 138–154. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65831-5_10
Binkhonain, M., Zhao, L.: A review of machine learning algorithms for identification and classification of non-functional requirements. Expert Syst. Appl.: X 1, 1–13 (2019)
Dalpiaz, F., van der Schalk, I., Brinkkemper, S., Aydemir, F.B., Lucassen, G.: Detecting terminological ambiguity in user stories: tool and experimentation. Inf. Softw. Technol. 110, 3–16 (2019)
Deocadez, R., Harrison, R., Rodriguez, D.: Automatically classifying requirements from app stores: a preliminary study. In: Fourth International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE (2017)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Galster, M., Angelov, S., Martínez-Fernández, S., Tofan, D.: Reference architectures in scrum: friends or foes? In: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), pp. 896–901. ACM (2017)
Gilson, F., Galster, M., Georis, F.: Extracting quality attributes from user stories for early architecture decision making. In: International Workshop on Decision Making in Software Architecture (MARCH), pp. 1–8. IEEE (2019)
ISO/IEC: ISO/IEC 25010 system and software quality models. Technical report, International Organization for Standardization/International Electrotechnical Commission (2010)
Koziolek, A.: Architecture-driven quality requirements prioritization. In: IEEE International Workshop on the Twin Peaks of Requirements and Architecture (TwinPeaks), pp. 1–5. IEEE (2012)
Lu, M., Liang, P.: Automatic classification of non-functional requirements from augmented app user reviews. In: International Conference on Evaluation and Assessment in Software Engineering (EASE), pp. 344–353. ACM (2017)
Martensson, T., Martini, A., Stahl, D., Bosch, J.: Continuous architecture: towards the goldilocks zone and away from vicious circles. In: International Conference on Software Architecture (ICSA), pp. 131–140. IEEE (2019)
Shahbazian, A., Lee, Y.K., Le, D., Brun, Y., Medvidovic, N.: Recovering architectural design decisions. In: International Conference on Software Architecture (ICSA), pp. 95–104. IEEE (2018)
Soliman, M., Galster, M., Riebisch, M.: Developing an ontology for architecture knowledge from developer communities. In: International Conference on Software Architecture (ICSA), pp. 89–92. IEEE (2017)
Waterman, M., Noble, J., Allan, G.: How much up-front? A grounded theory of agile architecture. In: International Conference on Software Engineering (ICSE), pp. 347–357. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Galster, M., Gilson, F., Georis, F. (2019). What Quality Attributes Can We Find in Product Backlogs? A Machine Learning Perspective. In: Bures, T., Duchien, L., Inverardi, P. (eds) Software Architecture. ECSA 2019. Lecture Notes in Computer Science(), vol 11681. Springer, Cham. https://doi.org/10.1007/978-3-030-29983-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-29983-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29982-8
Online ISBN: 978-3-030-29983-5
eBook Packages: Computer ScienceComputer Science (R0)