arXiv subjects

Mucahit Cevik

Publications and source records attributed to Mucahit Cevik.

41 records · Page 3Linked to original sources

arXiv2021

Does chronology matter in JIT defect prediction? A Partial Replication Study

Just-In-Time (JIT) models detect the fix-inducing changes (or defect-inducing changes). These models are designed based on the assumption that past code change properties are similar to future ones. However, as the system evolves, the expertise of developers and/or the complexity of the system also changes. In this work, we aim to investigate the effect of code change properties on JIT models over time. We also study the impact of using recent data as well as all available data on the performance of JIT models. Further, we analyze the effect of weighted sampling on the performance of fix-inducing properties of JIT models. For this purpose, we used datasets from Eclipse JDT, Mozilla, Eclipse Platform, and PostgreSQL. We used five families of change-code properties such as size, diffusion, history, experience, and purpose. We used Random Forest to train and test the JIT model and Brier Score and the area under the ROC curve for performance measurement. Our paper suggests that the predictive power of JIT models does not change over time. Furthermore, we observed that the chronology of data in JIT defect prediction models can be discarded by considering all the available data. On the other hand, the importance score of families of code change properties is found to oscillate over time. To mitigate the impact of the evolution of code change properties, it is recommended to use a weighted sampling approach in which more emphasis is placed upon the changes occurring closer to the current time. Moreover, since properties such as "Expertise of the Developer" and "Size" evolve with time, the models obtained from old data may exhibit different characteristics compared to those employing the newer dataset. Hence, practitioners should constantly retrain JIT models to include fresh data.

cs.SE↗

arXiv2021

Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study

Software defect prediction heavily relies on the metrics collected from software projects. Earlier studies often used machine learning techniques to build, validate, and improve bug prediction models using either a set of metrics collected within a project or across different projects. However, techniques applied and conclusions derived by those models are restricted by how identical those metrics are. Knowledge coming from those models will not be extensible to a target project if no sufficient overlapping metrics have been collected in the source projects. To explore the feasibility of transferring knowledge across projects without common labeled metrics, we systematically integrated Heterogeneous Defect Prediction (HDP) by replicating and validating the obtained results. Our main goal is to extend prior research and explore the feasibility of HDP and finally to compare its performance with that of its predecessor, Cross-Project Defect Prediction. We construct an HDP model on different publicly available datasets. Moreover, we propose a new ensemble voting approach in the HDP context to utilize the predictive power of multiple available datasets. The result of our experiment is comparable to that of the original study. However, we also explored the feasibility of HDP in real cases. Our results shed light on the infeasibility of many cases for the HDP algorithm due to its sensitivity to the parameter selection. In general, our analysis gives a deep insight into why and how to perform transfer learning from one domain to another, and in particular, provides a set of guidelines to help researchers and practitioners to disseminate knowledge to the defect prediction domain.

cs.SE↗

arXiv2020

Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting

Being able to interpret a machine learning model is a crucial task in many applications of machine learning. Specifically, local interpretability is important in determining why a model makes particular predictions. Despite the recent focus on AI interpretability, there has been a lack of research in local interpretability methods for time series forecasting while the few interpretable methods that exist mainly focus on time series classification tasks. In this study, we propose two novel evaluation metrics for time series forecasting: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold. These two metrics can measure the local fidelity of local explanation models. We extend the theoretical foundation to collect experimental results on two popular datasets, \textit{Rossmann sales} and \textit{electricity}. Both metrics enable a comprehensive comparison of numerous local explanation models and find which metrics are more sensitive. Lastly, we provide heuristical reasoning for this analysis.

cs.LG↗

arXiv2020

Explainable boosted linear regression for time series forecasting

Time series forecasting involves collecting and analyzing past observations to develop a model to extrapolate such observations into the future. Forecasting of future events is important in many fields to support decision making as it contributes to reducing the future uncertainty. We propose explainable boosted linear regression (EBLR) algorithm for time series forecasting, which is an iterative method that starts with a base model, and explains the model's errors through regression trees. At each iteration, the path leading to highest error is added as a new variable to the base model. In this regard, our approach can be considered as an improvement over general time series models since it enables incorporating nonlinear features by residuals explanation. More importantly, use of the single rule that contributes to the error most allows for interpretable results. The proposed approach extends to probabilistic forecasting through generating prediction intervals based on the empirical error distribution. We conduct a detailed numerical study with EBLR and compare against various other approaches. We observe that EBLR substantially improves the base model performance through extracted features, and provide a comparable performance to other well established approaches. The interpretability of the model predictions and high predictive accuracy of EBLR makes it a promising method for time series forecasting.

cs.LG↗

arXiv2018

Simultaneous optimization of isocenter locations and sector duration in radiosurgery

Stereotactic radiosurgery is an effective technique to treat brain tumors for which several inverse planning methods may be appropriate. We propose an integer programming model to simultaneous sector duration and isocenter optimization (SDIO) problem for Leksell Gamma Knife{\textregistered} Icon{\texttrademark} (Elekta, Stockholm, Sweden) to tractably incorporate treatment time. We devise a Benders decomposition scheme to solve the SDIO problem to optimality. The performances of our approaches are assessed using anonymized data from eight previously treated cases, and obtained treatment plans are compared against each other and against the clinical plans. The plans generated by our SDIO model all meet or exceed clinical guidelines while demonstrating high conformity.

physics.med-ph↗