arXiv subjects

Mucahit Cevik

Publications and source records attributed to Mucahit Cevik.

At least 37 records · Page 2Linked to original sources

arXiv2022

A Prompt-based Few-shot Learning Approach to Software Conflict Detection

A software requirement specification (SRS) document is an essential part of the software development life cycle which outlines the requirements that a software program in development must satisfy. This document is often specified by a diverse group of stakeholders and is subject to continual change, making the process of maintaining the document and detecting conflicts between requirements an essential task in software development. Notably, projects that do not address conflicts in the SRS document early on face considerable problems later in the development life cycle. These problems incur substantial costs in terms of time and money, and these costs often become insurmountable barriers that ultimately result in the termination of a software project altogether. As a result, early detection of SRS conflicts is critical to project sustainability. The conflict detection task is approached in numerous ways, many of which require a significant amount of manual intervention from developers, or require access to a large amount of labeled, task-specific training data. In this work, we propose using a prompt-based learning approach to perform few-shot learning for conflict detection. We compare our results to supervised learning approaches that use pretrained language models, such as BERT and its variants. Our results show that prompting with just 32 labeled examples can achieve a similar level of performance in many key metrics to that of supervised learning on training sets that are magnitudes larger in size. In contrast to many other conflict detection approaches, we make no assumptions about the type of underlying requirements, allowing us to analyze pairings of both functional and non-functional requirements. This allows us to omit the potentially expensive task of filtering out non-functional requirements from our dataset.

cs.SE↗

arXiv2022

ADPTriage: Approximate Dynamic Programming for Bug Triage

Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in issue tracking systems (ITS) offers only a limited solution and might easily fail when triagers must handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage task. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers' timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers' expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage solutions

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arXiv2022

Token Classification for Disambiguating Medical Abbreviations

Abbreviations are unavoidable yet critical parts of the medical text. Using abbreviations, especially in clinical patient notes, can save time and space, protect sensitive information, and help avoid repetitions. However, most abbreviations might have multiple senses, and the lack of a standardized mapping system makes disambiguating abbreviations a difficult and time-consuming task. The main objective of this study is to examine the feasibility of token classification methods for medical abbreviation disambiguation. Specifically, we explore the capability of token classification methods to deal with multiple unique abbreviations in a single text. We use two public datasets to compare and contrast the performance of several transformer models pre-trained on different scientific and medical corpora. Our proposed token classification approach outperforms the more commonly used text classification models for the abbreviation disambiguation task. In particular, the SciBERT model shows a strong performance for both token and text classification tasks over the two considered datasets. Furthermore, we find that abbreviation disambiguation performance for the text classification models becomes comparable to that of token classification only when postprocessing is applied to their predictions, which involves filtering possible labels for an abbreviation based on the training data.

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arXiv2022

Word-level Text Highlighting of Medical Texts for Telehealth Services

The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This creates additional work for medical professionals who are heavily dependent on medical data to complete their research and consult their patients. This paper aims to show how different text highlighting techniques can capture relevant medical context. This would reduce the doctors' cognitive load and response time to patients by facilitating them in making faster decisions, thus improving the overall quality of online medical services. Three different word-level text highlighting methodologies are implemented and evaluated. The first method uses TF-IDF scores directly to highlight important parts of the text. The second method is a combination of TF-IDF scores and the application of Local Interpretable Model-Agnostic Explanations to classification models. The third method uses neural networks directly to make predictions on whether or not a word should be highlighted. The results of our experiments show that the neural network approach is successful in highlighting medically-relevant terms and its performance is improved as the size of the input segment increases.

cs.LG↗

arXiv2022

Interpretable Time Series Clustering Using Local Explanations

This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to explain the decisions of the classification models. The explanations are used to obtain insights into the clustering models. We perform a detailed numerical study to test the proposed approach on multiple datasets, clustering models, and classification models. The analysis of the results shows that the proposed approach can be used to explain time series clustering models, specifically when the underlying classification model is accurate. Lastly, we provide a detailed analysis of the results, discussing how our approach can be used in a real-life scenario.

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arXiv2022

Improved $α$-GAN architecture for generating 3D connected volumes with an application to radiosurgery treatment planning

Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly from synthetic data generation due to data scarcity in the domain. However, medical image data is typically kept in 3D space, and generative models suffer from the curse of dimensionality issues in generating such synthetic data. In this paper, we investigate the potential of GANs for generating connected 3D volumes. We propose an improved version of 3D $α$-GAN by incorporating various architectural enhancements. On a synthetic dataset of connected 3D spheres and ellipsoids, our model can generate fully connected 3D shapes with similar geometrical characteristics to that of training data. We also show that our 3D GAN model can successfully generate high-quality 3D tumor volumes and associated treatment specifications (e.g., isocenter locations). Similar moment invariants to the training data as well as fully connected 3D shapes confirm that improved 3D $α$-GAN implicitly learns the training data distribution, and generates realistic-looking samples. The capability of improved 3D $α$-GAN makes it a valuable source for generating synthetic medical image data that can help future research in this domain.

eess.IV↗

arXiv2022

Auto Response Generation in Online Medical Chat Services

Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients. These services have become gradually popular over the years with the advent of necessary technological infrastructure. The benefits of telehealth have been even more apparent since the beginning of the COVID-19 crisis, as people have become less inclined to visit doctors in person during the pandemic. In this paper, we focus on facilitating the chat sessions between a doctor and a patient. We note that the quality and efficiency of the chat experience can be critical as the demand for telehealth services increases. Accordingly, we develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently, particularly during busy sessions. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months. We implement clustering algorithms to identify the most frequent responses by doctors and manually label the data accordingly. We then train machine learning algorithms using this preprocessed data to generate the responses. The considered algorithm has two steps: a filtering (i.e., triggering) model to filter out infeasible patient messages and a response generator to suggest the top-3 doctor responses for the ones that successfully pass the triggering phase. The method provides an accuracy of 83.28\% for precision@3 and shows robustness to its parameters.

cs.CL↗

arXiv2022

A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning

Sector duration optimization (SDO) is a problem arising in treatment planning for stereotactic radiosurgery on Gamma Knife. Given a set of isocenter locations, SDO aims to select collimator size configurations and irradiation times thereof such that target tissues receive prescribed doses in a reasonable amount of treatment time, while healthy tissues nearby are spared. We present a multiobjective linear programming model for SDO to generate a diverse collection of solutions so that clinicians can select the most appropriate treatment. We develop a generic two-phase solution strategy based on the epsilon-constraint method for solving multiobjective optimization models, which aims to systematically increase the number of high-quality solutions obtained, instead of conducting a traditional uniform search. To improve solution quality further and to accelerate the procedure, we incorporate some general and problem-specific enhancements. Moreover, we propose an alternative version of our two-phase strategy, which makes use of machine learning tools to reduce the computational effort. In our computational study on eight previously treated real test cases, a significant portion of obtained solutions outperformed clinical results and those from a single-objective model from the literature. In addition to significant benefits of the algorithmic enhancements, our experiments illustrate the usefulness of machine learning strategies to reduce the overall run times nearly by half while maintaining or besting the clinical practice.

math.OC↗

arXiv2022

Time Series Clustering for Grouping Products Based on Price and Sales Patterns

Developing technology and changing lifestyles have made online grocery delivery applications an indispensable part of urban life. Since the beginning of the COVID-19 pandemic, the demand for such applications has dramatically increased, creating new competitors that disrupt the market. An increasing level of competition might prompt companies to frequently restructure their marketing and product pricing strategies. Therefore, identifying the change patterns in product prices and sales volumes would provide a competitive advantage for the companies in the marketplace. In this paper, we investigate alternative clustering methodologies to group the products based on the price patterns and sales volumes. We propose a novel distance metric that takes into account how product prices and sales move together rather than calculating the distance using numerical values. We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns. We evaluate the performances of different clustering algorithms using our custom evaluation metric as well as Calinski Harabasz and Davies Bouldin indices, which are commonly used internal validity metrics. We conduct our numerical study using a propriety price dataset from an online food and grocery delivery company, and the publicly available Favorita sales dataset. We find that our proposed clustering approach and image clustering both perform well for finding the products with similar price and sales patterns within large datasets.

cs.LG↗

arXiv2022

S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems

Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study on four open-source software systems, namely, EclipseJDT, LibreOffice, GCC, and Mozilla, shows that taking into account the schedules of the developers decreases the average bug fixing times. We find that S-DABT leads to a high level of developer utilization through a fair distribution of the tasks among the developers and efficient use of the free spots in their schedules. Via the simulation of the issue tracking system, we also show how incorporating the schedule in the model formulation reduces the bug fixing time, improves the assignment accuracy, and utilizes the capability of each developer without much comprising in the model run times. We find that S-DABT decreases the complexity of the bug dependency graph by prioritizing blocking bugs and effectively reduces the infeasible assignment ratio due to bug dependencies. Consequently, we recommend considering developers' schedules while automating bug triage.

cs.SE↗

arXiv2022

Wayback Machine: A tool to capture the evolutionary behaviour of the bug reports and their triage process in open-source software systems

The issue tracking system (ITS) is a rich data source for data-driven decision-making. Different characteristics of bugs, such as severity, priority, and time to fix, provide a clear picture of an ITS. Nevertheless, such information may be misleading. For example, the exact time and the effort spent on a bug might be significantly different from the actual reporting time and the fixing time. Similarly, these values may be subjective, e.g., severity and priority values are assigned based on the intuition of a user or a developer rather than a structured and well-defined procedure. Hence, we explore the evolution of the bug dependency graph together with priority and severity levels to explore the actual triage process. Inspired by the idea of the "Wayback Machine" for the World Wide Web, we aim to reconstruct the historical decisions made in the ITS. Therefore, any bug prioritization or bug triage algorithms/scenarios can be applied in the same environment using our proposed ITS Wayback Machine. More importantly, we track the evolutionary metrics in the ITS when a custom triage/prioritization strategy is employed. We test the efficiency of the proposed algorithm using data extracted from three open-source projects. Our empirical study sheds light on the overlooked evolutionary metrics--e.g., overdue bugs and developers' loads--which are facilitated via our proposed past-event re-generator.

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arXiv2022

A Deep Reinforcement Learning Approach for the Meal Delivery Problem

We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day. A courier's duty is to pick-up an order from a restaurant and deliver it to a customer. We model this service as a Markov decision process and use deep reinforcement learning as the solution approach. We experiment with the resulting policies on synthetic and real-world datasets and compare those with the baseline policies. We also examine the courier utilization for different numbers of couriers. In our analysis, we specifically focus on the impact of the limited available resources in the meal delivery problem. Furthermore, we investigate the effect of intelligent order rejection and re-positioning of the couriers. Our numerical experiments show that, by incorporating the geographical locations of the restaurants, customers, and the depot, our model significantly improves the overall service quality as characterized by the expected total reward and the delivery times. Our results present valuable insights on both the courier assignment process and the optimal number of couriers for different order frequencies on a given day. The proposed model also shows a robust performance under a variety of scenarios for real-world implementation.

cs.LG↗

arXiv2021

On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies

Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodology is how uncertainty is incorporated into modeling. Traditionally, statistical forecasting techniques with simple forms, e.g., (first-order) autoregressive time-series models, are used to extract scenarios to be added to optimization models to represent the uncertain future. However, often times, the performance of these forecasting models are not thoroughly assessed. Motivated by the advances in probabilistic forecasting, we incorporate a deep learning-based time-series forecasting method into multistage stochastic programming framework, and compare it with the cases where a traditional forecasting method is employed to model the uncertainty. We assess the impact of more accurate forecasts on the quality of two commonly used look-ahead policies, a deterministic one and a two-stage one, in a rolling\red{-}horizon framework on a practical problem. Our results illustrate that more accurate forecasts contribute substantially to the model performance, and enable obtaining high-quality solutions even from computationally cheap heuristics. They also show that the probabilistic forecasting capabilities of deep learning-based methods can be especially beneficial when used as a (conditional) sampling tool for scenario-based models, and to predict the worst-case scenario for risk-averse models.

math.OC↗

arXiv2021

An empirical study on using CNNs for fast radio signal prediction

Accurate radio frequency power prediction in a geographic region is a computationally expensive part of finding the optimal transmitter location using a ray tracing software. We empirically analyze the viability of deep learning models to speed up this process. Specifically, deep learning methods including CNNs and UNET are typically used for segmentation, and can also be employed in power prediction tasks. We consider a dataset that consists of radio frequency power values for five different regions with four different frame dimensions. We compare deep learning-based prediction models including RadioUNET and four different variations of the UNET model for the power prediction task. More complex UNET variations improve the model on higher resolution frames such as 256x256. However, using the same models on lower resolutions results in overfitting and simpler models perform better. Our detailed numerical analysis shows that the deep learning models are effective in power prediction and they are able to generalize well to the new regions.

cs.LG↗

arXiv2021

VARGAN: Variance Enforcing Network Enhanced GAN

Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suffer from unstable training process, and mode collapse. In this paper, we introduce a new GAN architecture called variance enforcing GAN (VARGAN), which incorporates a third network to introduce diversity in the generated samples. The third network measures the diversity of the generated samples, which is used to penalize the generator's loss for low diversity samples. The network is trained on the available training data and undesired distributions with limited modality. On a set of synthetic and real-world image data, VARGAN generates a more diverse set of samples compared to the recent state-of-the-art models. High diversity and low computational complexity, as well as fast convergence, make VARGAN a promising model to alleviate mode collapse.

cs.LG↗

arXiv2021

Text Classification for Predicting Multi-level Product Categories

In an online shopping platform, a detailed classification of the products facilitates user navigation. It also helps online retailers keep track of the price fluctuations in a certain industry or special discounts on a specific product category. Moreover, an automated classification system may help to pinpoint incorrect or subjective categories suggested by an operator. In this study, we focus on product title classification of the grocery products. We perform a comprehensive comparison of six different text classification models to establish a strong baseline for this task, which involves testing both traditional and recent machine learning methods. In our experiments, we investigate the generalizability of the trained models to the products of other online retailers, the dynamic masking of infeasible subcategories for pretrained language models, and the benefits of incorporating product titles in multiple languages. Our numerical results indicate that dynamic masking of subcategories is effective in improving prediction accuracy. In addition, we observe that using bilingual product titles is generally beneficial, and neural network-based models perform significantly better than SVM and XGBoost models. Lastly, we investigate the reasons for the misclassified products and propose future research directions to further enhance the prediction models.

cs.IR↗

arXiv2021

DABT: A Dependency-aware Bug Triaging Method

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-prone. In this work, we introduce a bug triaging method, called Dependency-aware Bug Triaging (DABT), which leverages natural language processing and integer programming to assign bugs to appropriate developers. Unlike previous works that mainly focus on one aspect of the bug reports, DABT considers the textual information, cost associated with each bug, and dependency among them. Therefore, this comprehensive formulation covers the most important aspect of the previous works while considering the blocking effect of the bugs. We report the performance of the algorithm on three open-source software systems, i.e., EclipseJDT, LibreOffice, and Mozilla. Our result shows that DABT is able to reduce the number of overdue bugs up to 12\%. It also decreases the average fixing time of the bugs by half. Moreover, it reduces the complexity of the bug dependency graph by prioritizing blocking bugs.

cs.SE↗

arXiv2021

Predicting the Number of Reported Bugs in a Software Repository

The bug growth pattern prediction is a complicated, unrelieved task, which needs considerable attention. Advance knowledge of the likely number of bugs discovered in the software system helps software developers in designating sufficient resources at a convenient time. The developers may also use such information to take necessary actions to increase the quality of the system and in turn customer satisfaction. In this study, we examine eight different time series forecasting models, including Long Short Term Memory Neural Networks (LSTM), auto-regressive integrated moving average (ARIMA), and Random Forest Regressor. Further, we assess the impact of exogenous variables such as software release dates by incorporating those into the prediction models. We analyze the quality of long-term prediction for each model based on different performance metrics. The assessment is conducted on Mozilla, which is a large open-source software application. The dataset is originally mined from Bugzilla and contains the number of bugs for the project between Jan 2010 and Dec 2019. Our numerical analysis provides insights on evaluating the trends in a bug repository. We observe that LSTM is effective when considering long-run predictions whereas Random Forest Regressor enriched by exogenous variables performs better for predicting the number of bugs in the short term.

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