Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Editorial Iwsm-Mensura 2024 Proceedings Preface(CEUR-WS, 2024) Trudel, S.; Demirors, O.; Moulla, D.K.; Hacaloglu, T.[No abstract available]Conference Object Evaluating CUDA-Aware Approximate Computing Techniques(CEUR-WS, 2024) Öz, I.Approximate computing techniques offer performance improvements by performing inexact computations. Moreover, CUDA programs written to be executed on GPU devices employ specific features to utilize the parallel computation units of heterogeneous GPU architectures. While generic software-level approximate computing techniques have been applied to heterogeneous CUDA programs, CUDA-specific approaches may introduce promising performance improvements by not corrupting the target computations. In this work, we propose software approximation techniques for CUDA programs: kernel-aware loop perforation, partition-level synchronization, block-level atomic operations, and warp divergence elimination. We perform source code transformations on target benchmark programs by applying our techniques. We evaluate performance improvements by trading off accuracy in our target computations. Our experimental results reveal that CUDA-aware approximation techniques offer significant performance improvements at the expense of acceptable accuracy loss. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Conference Object Software Change Size Measurement: an Exploratory Systematic Mapping Study(CEUR-WS, 2024) Hacaloglu, T.; Demirörs, Onur; Küçükateş Ömüral, N.; Kılınç Soylu, G.; Demirörs, O.Change in software projects can occur through various channels. Customers may request modifications or new features; appraisal activities such as reviews or testing may uncover issues that necessitate adjustments, or products may need to adapt to changes in their operating environment. Therefore, it is essential to assess these changes explicitly and objectively within the scope of software engineering activities. Specifically, quantifying change by measuring its size is crucial for successful management, as without a meaningful metric, it is impossible to accurately assess its impact on the project's effort, schedule, and cost. This study aims to explore the concept of change in software engineering literature, with a particular emphasis on the methods used to measure its size. The study reveals that the current literature on this topic is still in its early stages and the measurement and estimation of changes remain challenging throughout both development and maintenance phases. According to the reviewed articles, size is primarily used for effort estimation. Various software artifacts from different stages of the Software Development Life Cycle (SDLC) serve as input for change measurement, highlighting the need for a versatile size measurement applicable across all SDLC phases. Most of the reviewed articles interpret change in the context of maintenance activities. This research sets a benchmark for the status of software size measures for software change and highlights related problems to suggest further research topics. © 2024 Copyright for this paper by its authors.Conference Object Explaining Graph Neural Network Predictions for Drug Repurposing(CEUR-WS, 2024) Loesch, J.; Yang, Y.; Ekmekci, P.; Dumontier, M.; Celebi, R.Graph Neural Networks (GNNs) are powerful tools for graph-related tasks, excelling in progressing graph-structured data while maintaining permutation invariance. However, their challenge lies in the obscurity of new node representations, hindering interpretability. This paper introduces a framework addressing this limitation by explaining GNN predictions. The proposed method takes any GNN prediction, for which it returns a concise subgraph as explanation. Utilizing Saliency Maps, an attribution gradient-based technique, we enhance interpretability by assigning importance scores to entities withing the knowledge graph via backpropagation. Evaluated on the Drug Repurposing Knowledge Graph, Graph Attention Network achieved a Hits@5 score of 0.451 and a Hits@10 score of 0.672. GraphSAGE demonstrated notable results with the highest recall rate of 0.992. Our framework underscores GNN efficacy and interpretability, which is crucial in complex scenarios like drug repurposing. Illustrated through an Alzheimer’s disease case study, our approach provides meaningful and comprehensible explanations for GNN predictions. This work contributes to advancing the transparency and utility of GNNs in real-world applications. © 2024 Copyright for this paper by its authors.Conference Object Citation - Scopus: 3Predicting Software Size and Effort From Code Using Natural Language Processing(CEUR-WS, 2024) Tenekeci, S.; Demirörs, Onur; Ünlü, H.; Dikenelli, E.; Selçuk, U.; Kılınç Soylu, G.; Demirörs, O.Software Size Measurement (SSM) holds a crucial role in software project management by facilitating the acquisition of software size, which serves as the primary input for development effort and schedule estimation. However, many small and medium-sized companies encounter challenges in conducting objective SSM and Software Effort Estimation (SEE) due to resource constraints and a lack of expert workforce. This often leads to inaccurate estimates and projects exceeding planned time and budget. Hence, organizations need to perform objective SSM and SEE with minimal resources and without relying on an expert workforce. In this research, we introduce two exploratory case studies aimed at predicting the functional size (COSMIC and Event-based size) and effort of software projects from the code using a deep-learning-based NLP model: CodeBERT. For this purpose, we collected and annotated two datasets consisting of 4800 Python and 1100 C# functions. Then, we trained a classification model to predict COSMIC data movements (entry, exit, read, write) and four regression models to predict Event-based size (interaction, communication, process) and effort. Despite utilizing a relatively small dataset for model training, we achieved promising results with an 84.5% accuracy for the COSMIC size, 0.13 normalized mean absolute error (NMAE) for the Event-based size, and 0.18 NMAE for the effort. These findings are particularly insightful as they demonstrate the practical utility of language models in SSM and SEE. © 2024 Copyright for this paper by its authors.Conference Object Evaluating Performance and Reliability of Selective Redundant Multithreading for Gpgpu Applications(CEUR-WS, 2021) Kaya,E.; Karadaş,O.F.; Öz,I.With the widespread use of GPU architectures in general-purpose computations, evaluating the soft error vulnerability of GPGPU programs and employing efficient fault tolerance techniques for more reliable execution becomes more prominent. Performing full redundancy, based on the redundant execution of the complete program, results in resource consumption and performance loss as well as energy inefficiency. Therefore, determining the most error-prone regions of the target program code and replicating only those parts maintains both high performance and acceptable error rates. In this study, we propose a partial redundant multithreading mechanism based on the soft error vulnerability of GPGPU applications and perform a trade-off analysis between performance and reliability. Firstly, we perform fault injection experiments to evaluate the SDC rates for each kernel function. Then, based on the outcome of the fault injection experiments, we determine the kernel function to-be-replicated. According to the pragmas denoting the redundancy points in the source code, our custom LLVM pass generates the code that enables the redundant execution for the specified code region. We evaluate both the reliability and performance of the redundant execution scenarios measuring the execution time of the redundant program generated by our compiler-managed redundancy technique. Our results demonstrate that protecting only the most vulnerable kernel functions enables high reliability without hurting the performance significantly. © 2021 The Authors.Conference Object Yazilim Yapisal Kapsama Analizinde Testlerin Önceliklendirilmesi(CEUR-WS, 2015) Ayav,T.[No abstract available]Conference Object Conference Object Size Measurement and Effort Estimation in Microservicebased Projects: Results From Pakistan(CEUR-WS, 2023) Soylu, Görkem Kılınç; Ünlü, Hüseyin; Ahmad, Isra Shafique; Demirörs, OnurDuring the last decade, microservice-based software architecture has been a common design paradigm in the industry and has been successfully utilized by organizations. Microservice-based software architecture, specifically in the form of reactive systems, has substantial differences from the more conventional design paradigms, such as the object-oriented paradigm. The architecture moved away from being data-driven and evolved into a behavior-oriented structure. The usage of a single database is replaced by the structures in which each microservice is developed independently and has its own database. Therefore, adaptation demands software organizations to transform their culture. In this study, we aimed to get an insight into how Pakistani software organizations perform size measurement and effort estimation in their software projects which embrace the microservice-based software architecture paradigm. For this purpose, we surveyed 49 Pakistani participants from different agile organizations over different roles and domains to collect information on their experience in microservice-based projects. Our results reveal that although Pakistani organizations face challenges, they continue using familiar subjective size measurement and effort estimation approaches that they have used for traditional architectures. © 2023 Copyright for this paper by its authors.Conference Object Citation - Scopus: 1A Survey on Cosmic Students Estimation Challenge(CEUR-WS, 2022) Hacaloğlu, Tuna; Say, Bilge; Ünlü, Hüseyin; Küçükateş Ömüral, Neslihan; Demirörs, OnurSoftware project management is a significant software engineering practice that is highly related to achieving software-specific project goals. This study aims to share students’ perceptions of incorporating an international software estimation challenge called “COSMIC Students’ Estimation Challenge” into a software project management course. For this aim, students were taught the COSMIC Functional Size Measurement method and entered the competition. After the competition, a questionnaire asking for the students’ opinions was collected. The objective of the research is to get an insight into to what extent incorporating this type of competition activity -a challenge- can contribute to students’ learning perceptions. In the long run, the findings can contribute to creating a foresight about making the necessary curriculum arrangements to form a more up-to-date and dynamic education plan by including the methods applied in the software industry in Software Engineering education. The results suggest that this kind of competition experience and preparation is helpful for students to learn the COSMIC method.