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Displaying 1-18 out of 18 total
What Do We Know about Test-Driven Development?
Found in: IEEE Software
By Forrest Shull, Grigori Melnik, Burak Turhan, Lucas Layman, Madeline Diep, Hakan Erdogmus
Issue Date:November 2010
pp. 16-19
TDD proponents assert that frequent, incremental testing not only improves the delivered code's quality but also generates a cleaner design. The authors present results from a systematic literature review as well as commentary on the results from a TDD exp...
Constructing Defect Predictors and Communicating the Outcomes to Practitioners
Found in: 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
By Taneli Taipale,Mika Qvist,Burak Turhan
Issue Date:October 2013
pp. 357-362
Background: An alternative to expert-based decisions is to take data-driven decisions and software analytics is the key enabler for this evidence-based management approach. Defect prediction is one popular application area of software analytics, however wi...
A Replicated Experiment on the Effectiveness of Test-First Development
Found in: 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
By Davide Fucci,Burak Turhan
Issue Date:October 2013
pp. 103-112
Background: Test-first development (TF) is regarded as a development practice that can lead to better quality of software products, as well as improved developer productivity. By implementing unit tests before the corresponding production code, the tests t...
Message from the PROMISE 2013 Chairs
Found in: 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
By Burak Turhan,Stefan Wagner,Ayse Bener,Massimiliano Di Penta,Ye Yang
Issue Date:October 2013
pp. 394
PROMISE conference is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of prediction models in software engineering. Such models could be targete...
Empirical Evaluation of Mixed-Project Defect Prediction Models
Found in: Software Engineering and Advanced Applications, Euromicro Conference
By Burak Turhan,Ayse Tosun,Ayse Bener
Issue Date:September 2011
pp. 396-403
Defect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects. On the other hand, recent studies try to utilize data across projects for building defect prediction models. We combine both approac...
Software Defect Prediction Using Call Graph Based Ranking (CGBR) Framework
Found in: Software Engineering and Advanced Applications, Euromicro Conference
By Burak Turhan, Gozde Kocak, Ayse Bener
Issue Date:September 2008
pp. 191-198
Recent research on static code attribute (SCA) based defect prediction suggests that a performance ceiling has been achieved and this barrier can be exceeded by increasing the information content in data. In this research we propose static call graph based...
A Multivariate Analysis of Static Code Attributes for Defect Prediction
Found in: Quality Software, International Conference on
By Burak Turhan, Ayse Bener
Issue Date:October 2007
pp. 231-237
Defect prediction is important in order to reduce test times by allocating valuable test resources effectively. In this work, we propose a model using multivariate approaches in conjunction with Bayesian methods for defect predictions. The motivation behin...
Evaluation of Feature Extraction Methods on Software Cost Estimation
Found in: Empirical Software Engineering and Measurement, International Symposium on
By Burak Turhan, Onur Kutlubay, Ayse Bener
Issue Date:September 2007
pp. 497
This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use Principal Component Analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods wi...
A Two-Step Model for Defect Density Estimation
Found in: EUROMICRO Conference
By Onur Kutlubay, Burak Turhan, Ayse B. Bener
Issue Date:August 2007
pp. 322-332
<p>Identifying and locating defects in software projects is a difficult task. Further, estimating the density of defects is more difficult. Measuring software in a continuous and disciplined manner brings many advantages such as accurate estimation o...
A Template for Real World Team Projects for Highly Populated Software Engineering Classes
Found in: Software Engineering, International Conference on
By Burak Turhan, Ayse Bener
Issue Date:May 2007
pp. 748-753
Assigning projects of group work in the context of software engineering courses has become a commonly used practice in several educational institutions. Previously reported results examined different aspects of this approach. The problem is that most studi...
Mining Software Data
Found in: Data Engineering Workshops, 22nd International Conference on
By Burak Turhan, Onur Kutlubay
Issue Date:April 2007
pp. 912-916
Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of software development processes and the final software product. Number of researches ...
Local versus Global Lessons for Defect Prediction and Effort Estimation
Found in: IEEE Transactions on Software Engineering
By Tim Menzies,Andrew Butcher,David Cok,Andrian Marcus,Lucas Layman,Forrest Shull,Burak Turhan,Thomas Zimmermann
Issue Date:June 2013
pp. 822-834
Existing research is unclear on how to generate lessons learned for defect prediction and effort estimation. Should we seek lessons that are global to multiple projects or just local to particular projects? This paper aims to comparatively evaluate local v...
Dione: an integrated measurement and defect prediction solution
Found in: Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering (FSE '12)
By Ayse Bener, Ayse Tosun Misirli, Bora Caglayan, Burak Turhan, Gul Calikli, Turgay Aytac
Issue Date:November 2012
pp. 1-2
We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and anal...
Factors characterizing reopened issues: a case study
Found in: Proceedings of the 8th International Conference on Predictive Models in Software Engineering (PROMISE '12)
By Andriy Miranskyy, Ayse Bener, Ayse Tosun Misirli, Bora Caglayan, Burak Turhan
Issue Date:September 2012
pp. 1-10
Background: Reopened issues may cause problems in managing software maintenance effort. In order to take actions that will reduce the likelihood of issue reopening the possible causes of bug reopens should be analysed. Aims: In this paper, we investigate p...
How to build repeatable experiments
Found in: Proceedings of the 5th International Conference on Predictor Models in Software Engineering (PROMISE '09)
By Bojan Cukic, Burak Turhan, Gregory Gay, Tim Menzies
Issue Date:May 2009
pp. 48-54
The mantra of the PROMISE series is "repeatable, improvable, maybe refutable" software engineering experiments. This community has successfully created a library of reusable software engineering data sets. The next challenge in the PROMISE community will b...
ENNA: software effort estimation using ensemble of neural networks with associative memory
Found in: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering (SIGSOFT '08/FSE-16)
By Ayse Basar Bener, Burak Turhan, Yigit Kultur
Issue Date:November 2008
pp. 1-2
Companies usually have limited amount of data for effort estimation. Machine learning methods have been preferred over parametric models due to their flexibility to calibrate the model for the available data. On the other hand, as machine learning methods ...
Ensemble of software defect predictors: a case study
Found in: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (ESEM '08)
By Ayse Bener, Ayse Tosun, Burak Turhan
Issue Date:October 2008
pp. 1-87
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 classifie...
Implications of ceiling effects in defect predictors
Found in: Proceedings of the 4th international workshop on Predictor models in software engineering (PROMISE '08)
By Ayse Bener, Bojan Cukic, Burak Turhan, Gregory Gay, Tim Menzies, Yue Jiang
Issue Date:May 2008
pp. 45-46
Context: There are many methods that input static code features and output a predictor for faulty code modules. These data mining methods have hit a "performance ceiling"; i.e., some inherent upper bound on the amount of information offered by, say, static...