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 software measurement program and to predict pre-release defects. Based on our prior publication, we have shared our experience in terms of the project steps (i.e. challenges and opportunities). We have further introduced new techniques that improve our earlier results. Objective: In our previous work, we have built similar predictors using data representative for U.S. software development. Our task here was to check if those predictors were specific solely to U.S. organizations or to a broader class of software. Method: We have presented our approach and results in the form of an experience report. Specifically, we have made use of different techniques for improving the information content of the software data and the performance of a Naïve Bayes classifier in the prediction model that is locally tuned for the company. We have increased the information content of the software data by using module dependency data and improved the performance by adjusting the hyper parameter (decision threshold) of the Naïve Bayes classifier. We have reported and discussed our results in terms of defect detection rates and false alarms. We also carried out a cost-benefit analysis to show that our approach can be efficiently put into practice. Results: Our general result is that general defect predictors, which exist across a wide range of software (in both U.S. and Turkis
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