Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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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.Article Link Prediction for Completing Graphical Software Models Using Neural Networks(IEEE, 2023) Leblebici, Onur; Tuğlular, Tuğkan; Belli, FevziDeficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models. AuthorArticle Citation - WoS: 1Citation - Scopus: 1How Software Practitioners Perceive Work-Related Barriers and Benefits Based on Their Educational Backgrounds: Insights From a Survey Study(IEEE, 2023) Ünlü, Hüseyin; Yürüm, Ozan Raşit; Özcan Top, Özden; Demirörs, OnurSurvey results show that software practitioners from nonsoftware-related backgrounds face more barriers, have fewer benefits, and feel less satisfied in their work life. However, these differences reduce with more than 10 years of experience and involvement in software-related graduate programs, certificates, and mentorship.Conference Object Kurt saldırıları için sentetik irislerde örnek seçilimi(IEEE, 2023) Akdeniz, Eyüp Kaan; Erdoğmuş, NesliIn this study, samples with higher potential to succeed in wolf attacks are picked among synthetically generated iris images, and the composed subset is shown to pose a more significant threat toward an iris recognition system backed by a Presentation Attack Detection (PAD) module with respect to randomly selected samples. Iris images generated by Deep Convolutional Generative Adversarial Networks (DCGAN) are firstly filtered by rejection sampling on PAD score distribution of real iris image PAD scores. Next, the probability of zero success in all attack attempts is calculated for each synthetic iris image, using real iris images in the training set, and match and non-match score distributions are calculated on those. Synthetic images with the lowest probabilities of zero success are included in the final set. Our hypothesis that this set would be more successful in wolf attacks is tested by comparing its spoofing performances with randomly selected sample sets.Article Spectral Test Generation for Boolean Expressions(World Scientific Publishing, 2023) Ayav, TolgaThis paper presents a novel method for testing Boolean expressions. It is based on spectral, aka Fourier analysis of Boolean functions which is exploited to generate test inputs. The approach has three important contributions: (i) It generates a relatively small test suite with a high capability of fault detection, (ii) The test suite is prioritized such that expected fault detection time is shorter, (iii) It is entirely mathematical relying on a simple and straightforward formula. The proposed method is formulated and evaluations are performed on both synthetic and real expressions. It is also compared with two common test generation criteria, MC/DC and Minimal MUMCUT. Evaluations show that the test suite generated by the spectral approach is relatively small while expressing the capability of a better and quicker fault detection. The approach presented in this paper provides a useful insight into how spectral/Fourier analysis of Boolean functions can be exploited in software testing.Article Citation - WoS: 2Citation - Scopus: 3Mutation-Based Minimal Test Suite Generation for Boolean Expressions(World Scientific Publishing, 2023) Ayav, Tolga; Belli, FevziBoolean expressions are highly involved in control flows of programs and software specifications. Coverage criteria for Boolean expressions aim at producing minimal test suites to detect software faults. There exist various testing criteria, efficiency of which is usually evaluated through mutation analysis. This paper proposes an integer programming-based minimal test suite generation technique relying on mutation analysis. The proposed technique also takes into account the cost of fault detection. The technique is optimal such that the resulting test suite guarantees to detect all the mutants under given fault assumptions, while maximizing the average percentage of fault detection of a test suite. Therefore, the approach presented can also be considered as a reference method to check the efficiency of any common technique. The method is evaluated using four well-known real benchmark sets of Boolean expressions and is also exemplary compared with MCDC criterion. The results show that the test suites generated by the proposed method provide better fault coverage values and faster fault detection.Article Studying the Co-Evolution of Source Code and Acceptance Tests(World Scientific Publishing, 2023) Yalçın, Ali Görkem; Tuğlular, TuğkanTesting is a vital part of achieving good-quality software. Deploying untested code can cause system crashes and unexpected behavior. To reduce these problems, testing should evolve with coding. In addition, test suites should not remain static throughout the software versions. Since whenever software gets updated, new functionalities are added, or existing functionalities are changed, test suites should be updated along with the software. Software repositories contain valuable information about the software systems. Access to older versions and differentiating adjacent versions' source code and acceptance test changes can provide information about the evolution process of the software. This research proposes a method and implementation to analyze 21 open-source real-world projects hosted on GitHub regarding the co-evolution of both software and its acceptance test suites. Related projects are retrieved from repositories, their versions are analyzed, graphs are created, and analysis related to the co-evolution process is performed. Observations show that the source code is getting updated more frequently than the acceptance tests. They indicate a pattern that source code and acceptance tests do not evolve together. Moreover, the analysis showed that a few acceptance tests test most of the functionalities that take a significant line of code.Article Citation - WoS: 3Citation - Scopus: 4Application of the Law of Minimum and Dissimilarity Analysis To Regression Test Case Prioritization(IEEE, 2023) Ufuktepe, Ekincan; Tuğlular, TuğkanRegression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods. To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.Article Citation - Scopus: 1An Interestingness Measure for Knowledge Bases(Elsevier, 2023) Oğuz, Damla; Soygazi, FatihAssociation rule mining and logical rule mining both aim to discover interesting relationships in data or knowledge. In association rule mining, relationships are identified based on the occurrence of items in a dataset, while in logical rule mining, relationships are determined based on logical relationships between atoms in a knowledge base. Association rule mining has been widely studied in transactional databases, mainly for market basket analysis. Confidence has become the most widely used interesting measure to assess the strength of a rule. Many other interestingness measures have been proposed since confidence can be insufficient to filter negatively associated relationships. Recently, logical rule mining has become an important area of research, as new facts can be inferred by applying discovered logical rules. They can be used for reasoning, identifying potential errors in knowledge bases, and to better understand data. However, there are currently only a few measures for logical rule mining. Furthermore, current measures do not consider relations that can have several objects, called quasi-functions, which can dramatically alter the interestingness of the rule. In this paper, we focus on effectively assessing the strength of logical rules. We propose a new interestingness measure that takes into account two categories of relations, functions and quasi-functions, to assess the degree of certainty of logical rules. We compare our proposed measure with a widely used measure on both synthetic test data and real knowledge bases. We show that it is more effective in indicating rule quality, making it an appropriate interestingness measure for logical rule evaluation. & COPY; 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Article Citation - WoS: 3Citation - Scopus: 4Scalable Rfid Authentication Protocol Based on Physically Unclonable Functions(Elsevier, 2023) Kurt, Işıl; Alagöz, Fatih; Akgün, MeteRadio Frequency Identification (RFID) technology is commonly used for tracking and identifying objects. However, this technology poses serious security and privacy concerns for individuals carrying the tags. To address these issues, various security protocols have been proposed. Unfortunately, many of these solutions suffer from scalability problems, requiring the back-end server to work linearly in the number of tags for a single tag identification. Some protocols offer O(1) or O(log n) identification complexity but are still susceptible to serious attacks. Few protocols consider attacks on the reader-side. Our proposed RFID authentication protocol eliminates the need for a search in the back-end and leverages Physically Unclonable Functions (PUFs) to securely store tag secrets, making it resistant to tag corruption attacks. It provides constant-time identification without sacrificing privacy and offers log2 n times better identification performance than the state-of-the-art protocol. It ensures destructive privacy for tag holders in the event of reader corruption without any conditions. Furthermore, it enables offline readers to maintain destructive privacy in case of corruption.