Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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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: 1Monocular Vision-Based Prediction of Cut-In Manoeuvres With Lstm Networks(Springer, 2023) Nalçakan, Yağız; Baştanlar, YalınAdvanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. In this paper, we first discuss the importance of predicting dangerous lane changes and provide its description as a machine learning problem. After summarizing the previous work, we propose a method to predict potentially dangerous lane changes (cut-ins) of the vehicles in front. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera, and we classify the target vehicle’s maneuver based on the recent video frames. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step. It is computationally efficient compared to other vision-based methods since it exploits a small number of features for the classification step rather than feeding CNNs with RGB frames. We evaluated our approach on a publicly available driving dataset and a lane change detection dataset. We obtained 0.9585 accuracy with the side-aware two-class (cut-in vs. lane-pass) classification model. Experiment results also reveal that our approach outperforms state-of-the-art approaches when used for lane change detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Article Citation - Scopus: 5Unifying Behavioral and Feature Modeling for Testing of Software Product Lines(World Scientific Publishing, 2023) Belli, Fevzi; Tuğlular, Tuğkan; Ufuktepe, EkincanExisting software product line (SPL) engineering testing approaches generally provide positive testing that validates the SPL's functionality. Negative testing is commonly neglected. This research aims to unify behavioral and feature models of an SPL, enable testing before and after variability binding for domain-centric and product-centric testing, and combine positive and negative testing for a holistic testing view. This study suggests behavioral modeling with event sequence graphs (ESGs). This heterogeneous modeling strategy supports bottom-up domain testing and top-down product testing with the feature model. This new feature-oriented ESG test creation method generates shorter test sequences than the original ESG optimum test sequences. Statechart and original ESG test-generating methods are compared. Positive testing findings are similar. The Statechart technique generated 12 test cases with 59 events, whereas the ESG technique created six test cases with 60 events. The ESG technique generated 205 negative test cases with 858 events with the Test Suite Designer tool. However, the Conformiq Designer tool for the Statechart technique does not have a negative test case generation capability. It is shown that the proposed ESG-based holistic approach confirms not only the desirable (positive) properties but also the undesirable (negative) ones. As an additional research, the traditional ESG test-generating approach is compared to the new feature-oriented method on six SPLs of different sizes and features. Our case study results show that the traditional ESG test generation approach demonstrated higher positive test generation scores compare to the proposed feature-oriented test generation approach. However, our proposed feature-oriented test generation approach is capable of generating shorter test sequences, which could be beneficial for reducing the execution time of test cases compared to traditional ESG approach. Finally, our case study has also shown that regardless of the test generation approach, there has been found no significant difference between the Bottom-up and Top-down test strategies with respect to their positive test generation scores. © World Scientific Publishing Company.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.Conference Object A Size Measurement Method for Enterprise Applications(CEUR-WS, 2022) Küçükateş Ömüral, Neslihan; Demirörs, OnurEnterprise Applications are known as one of the best practices of software reuse. They are complex applications, including most of the business processes. In this domain, size measurements and effort predictions are mostly performed in an ad-hoc fashion, and they frequently suffer from schedule and budget overruns. We developed a size measurement method for Enterprise Applications and explained this novel method in this paper. We categorized transactions as “unchanged”, “changed”, and “new” in this method. We defined a size measurement unit, Data Transaction Point (DTP), and measured size as DTP in these categories. We conducted a sample size measurement with a well-known business process to demonstrate the implementation of the method.Conference Object Citation - WoS: 2Citation - Scopus: 3Impact of Variations in Synthetic Training Data on Fingerprint Classification(IEEE, 2019) İrtem, Pelin; İrtem, Emre; Erdoğmuş, NesliCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.Conference Object Citation - Scopus: 1A Novel Feature To Predict Buggy Changes in a Software System(Springer, 2022) Yılmaz, Rahime; Nalçakan, Yağız; Haktanır, ElifResearchers have successfully implemented machine learning classifiers to predict bugs in a change file for years. Change classification focuses on determining if a new software change is clean or buggy. In the literature, several bug prediction methods at change level have been proposed to improve software reliability. This paper proposes a model for classification-based bug prediction model. Four supervised machine learning classifiers (Support Vector Machine, Decision Tree, Random Forrest, and Naive Bayes) are applied to predict the bugs in software changes, and performance of these four classifiers are characterized. We considered a public dataset and downloaded the corresponding source code and its metrics. Thereafter, we produced new software metrics by analyzing source code at class level and unified these metrics with the existing set. We obtained new dataset to apply machine learning algorithms and compared the bug prediction accuracy of the newly defined metrics. Results showed that our merged dataset is practical for bug prediction based experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Editorial Message From Tain Symposium Organizing Committee(Institute of Electrical and Electronics Engineers Inc., 2015) Tuğlular, Tuğkan; Cai, Y.; Dustdar, S.; Yamazaki, K.It is a pleasure to welcome you to COMPSAC 2015 Symposium on Technologies and Applications of the Internet (TAIN). This year, first time in its history, COMPSAC is organized as a series of symposiums under the main theme of “Mobile and Cloud Systems – Challenges and Applications”. TAIN Symposium providing a forum to share latest innovations on Internet technologies and applications perfectly matches with the main theme. Topics of interest for TAIN include all types of networks with their architectures and applications as well as their management, performance, and security. Moreover, TAIN Symposium addresses Internet of things (IoT), machine-to-machine (M2M) and peer-to-peer (P2P) communications, content distribution networks (CDN) and also emerging network technologies such as software defined networks (SDN). A vast array of challenges for Internet technologies and applications and proposals for solutions will be discussed in TAIN Symposium.Conference Object Cosmic Light Vs Cosmic Classic Manual: Case Studies in Functional Size Measurement(CEUR Workshop Proceedings, 2020) Hacaloğlu, Tuna; Ünlü, Hüseyin; Demirörs, Onur; Abran, AlainFunctional size has been used in software engineering for more than 40 years. When measured early in the software development life cycle, it can serve as direct input for effort estimation. The COSMIC Functional Size Measurement (FSM) method developed by the Common Software Measurement Consortium (COSMIC) is the latest ISO-compliant functional sizing method. A streamlined manual titled ''Software Development Velocity with COSMIC Function Points'' summarizes the measurement process and shortens the learning time. The aim of this study is to compare the classic COSMIC FSM manual and this new “light” manual in terms of accuracy of the resulting FSM applied to case studies. The findings show that use of the light manual results in accurate measurement. In addition, there were no significant time differences between the two. With respect to the variations in COSMIC Function Points (CFP) values in the two case studies, they three causes were identified: the Object of Interest (OOI) concept and corresponding data groups, details regarding Functional Process Independence, and Error/Confirmation messages related to the scope of the information included in the manuals. Copyright © 2020 for this paper by its authors.Conference Object Challenges and Working Solutions in Agile Adaptation: Experiences From the Industry(CEUR Workshop Proceedings, 2020) Özcan Top, Özden; Demirörs, Onur; McCaffery, FergalChallenges in agile adaptation is inevitable in software development projects and have to be dealt with by software practitioners. The pathway to excellence in agility requires experience of challenges, failure of process scenarios; and the discovery of working solutions by software development teams. The major purpose of this study is to highlight both the challenges organizations faced when implementing agile techniques and the solutions adopted that proved successful. In order to specify these challenges and working solutions, we performed a multiple case study by using the Software Agility Assessment Reference Model (AgilityMod). In this paper, we describe two cases that achieve the highest levels of agility among eight cases and describe their experiences in achieving a good adaptation through the challenges that they faced and the solutions that were found for these challenges. Additionally, we provide two challenges that have not been resolved yet and are subject to further discussions. Copyright © 2020 for this paper by its authors.