Küçük bir kurumda ürün hatalılık tahmini için süreç işleteme verisinin kullanıldığı bir metot.

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Türkiye

Tezin Onay Tarihi: 2012

Tezin Dili: İngilizce

Öğrenci: Damla Sivrioğlu

Eş Danışman: Onur Demirörs, ONUR DEMİRÖRS

Özet:

As a part of the quality management, product defectiveness prediction is vital for small software organizations as for instutional ones. Although for defect prediction there have been conducted a lot of studies, process enactment data cannot be used because of the difficulty of collection. Additionally, there is no proposed approach known in general for the analysis of process enactment data in software engineering. In this study, we developed a method to show the applicability of process enactment data for defect prediction and answered “Is process enactment data beneficial for defect prediction?”, “How can we use process enactment data?” and “Which approaches and analysis methods can our method support?” questions. We used multiple case study design and conducted case studies including with and without process enactment data in a small software development company. We preferred machine learning approaches rather than statistical ones, in order to cluster the data which includes process enactment informationsince we believed that they are convenient with the pattern oriented nature of the data. By the case studies performed, we obtained promising results. We evaluated performance values of prediction models to demonstrate the advantage of using process enactment data for the prediction of defect open duration value. When we have enough data points to apply machine learning methods and the data can be clusteredhomogeneously, we observed approximately 3% (ranging from -10% to %17) more accurate results from analyses including with process enactment data than the without ones. Keywords: