Customer requests matter: Early stage software effort estimation using k-grams

Kazlm Klvanç Eren, Can Ozbey, Beyza Eken, Ayşe Tosun

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

1 Atıf (Scopus)

Özet

Estimating the software development effort associated with a customer request, immediately after the request has been made, is quite challenging for project managers. Studies on software effort estimation often utilize expert knowledge to build statistical or machine learning models, although it is subjective and human-dependent. Rich text, natural language based descriptions provided by the customers are mostly not incorporated into the models during estimation. The aim of this study is to propose an early stage software effort estimation model that can predict the effort of the related problem immediately after a customer request or bug fix problem is appeared. Our model utilizes the textual descriptions of customer requests collected in the requirement management tool, and other features stored with the customer requests. Our results show that the use of textual data helps to make better predictions for an effort estimation system. Besides the effect of textual data, we asked whether there is a significant difference between the performance of an effort estimation system that can be used for all customers (Unified Model) and Customer Specific Models which is trained using only the related customer's requests. The Pred(25), Standard Accuracy (SA) and Gibbs' A were used during the evaluation. The Unified Model for early stage effort estimation achieves 43% pred(25) and 51% SA values. Customer Specific Models, on the other hand, depending on the customer, obtain 39%-58% Pred(25), and 35%-58% SA values. The results show that there is no significant advantage between the Unified Model and Customer Specific Models, and which model to use should be determined according to the customer.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı35th Annual ACM Symposium on Applied Computing, SAC 2020
YayınlayanAssociation for Computing Machinery
Sayfalar1540-1547
Sayfa sayısı8
ISBN (Elektronik)9781450368667
DOI'lar
Yayın durumuYayınlandı - 30 Mar 2020
Etkinlik35th Annual ACM Symposium on Applied Computing, SAC 2020 - Brno, Czech Republic
Süre: 30 Mar 20203 Nis 2020

Yayın serisi

AdıProceedings of the ACM Symposium on Applied Computing

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???event.eventtypes.event.conference???35th Annual ACM Symposium on Applied Computing, SAC 2020
Ülke/BölgeCzech Republic
ŞehirBrno
Periyot30/03/203/04/20

Bibliyografik not

Publisher Copyright:
© 2020 ACM.

Finansman

This research is partially funded by the The Scientific and Technological Research Council of Turkey (TSB-TAK) under grant number 3170385. This research is partially funded by the The Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant number 3170385.

FinansörlerFinansör numarası
TSB-TAK
TÜBİTAK3170385
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

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