Learning to aggregate: an automated aggregation method for software quality model

Meng Yan, Xiaohong Zhang, Chao Liu, Jie Zou, Ling Xu, Xin Xia

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)


Quality models are regarded as a well-acceptedapproach for assessing high-level abstract quality characteristics(e.g., maintainability) by aggregation from low-level metrics. However, most of the existing quality models adopt the weightedlinear aggregation method which suffers from a lack of consensusin how to decide the correct weights. To address this issue, wepresent an automated aggregation method which adopts a kind ofprobabilistic weight instead of the subjective weight in previousaggregation methods. In particular, we utilize a topic modelingtechnique to estimate the probabilistic weight by learning froma software benchmark. In this manner, our approach can enableautomated quality assessment by using the learned knowledgewithout manual effort. In addition, we conduct an application onthe maintainability assessment of the systems in our benchmark. The result shows that our approach can reveal the maintainabilitywell through a correlation analysis with the changed lines of code.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering Companion, ICSE-C 2017
Subtitle of host publication20–28 May 2017 Buenos Aires, Argentina
EditorsAlessandro Orso, Martin Robillard
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages3
ISBN (Electronic)9781538615898
ISBN (Print)9781538615904
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Software Engineering 2017 - Buenos Aires, Argentina
Duration: 20 May 201728 May 2017
Conference number: 39th


ConferenceInternational Conference on Software Engineering 2017
Abbreviated titleICSE-C 2017
CityBuenos Aires
OtherIEEE/ACM International Conference on Software Engineering Companion (ICSE-C 2017)
Internet address

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