Ayse Tosun Misirli

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In ICSE'08, Zimmermann and Nagappan show that network measures derived from dependency graphs are able to identify critical binaries of a complex system that are missed by complexity metrics. The system used in their analysis is a Windows product. In this study, we conduct additional experiments on public data to reproduce and validate their results. We use(More)
Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow(More)
<i>Background</i>: Most of the experiments in software engineering (SE) employ students as subjects. This raises concerns about the realism of the results acquired through students and adaptability of the results to software industry. <i>Aim</i>: We compare students and professionals to understand how well students represent professionals as experimental(More)
We have conducted a study in a large telecommunication company in Turkey to employ a software measurement program and to predict pre-release defects. We have previously built such predictors using AI techniques. This project is a transfer of our research experience into a real life setting to solve a specific problem for the company: to improve code quality(More)
This paper shares our experience with initial negotiation and topic elicitation process for conducting industry experiments in six software development organizations in Finland. The process involved interaction with company representatives in the form of both multiple group discussions and separate face-to-face meetings. Fitness criteria developed by(More)
As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most time-consuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In this(More)
Context: Building defect prediction models in large organizations has many challenges due to limited resources and tight schedules in the software development lifecycle. It is not easy to collect data, utilize any type of algorithm and build a permanent model at once. We have conducted a study in a large telecommunications company in Turkey to employ a(More)
In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also(More)
We present an integrated measurement and defect prediction tool: <i>Dione</i>. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple(More)