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.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 - WoS: 2Citation - Scopus: 2Dynamic Itemset Mining Under Multiple Support Thresholds(IOS Press, 2016) Abuzayed, Nourhan; Ergenç Bostanoğlu, Belgin; Ergenç, BelginHandling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset mining algorithms. Existing dynamic itemset mining algorithms are devised for single support threshold whereas multiple support threshold algorithms assume that the databases are static. This paper focuses on dynamic update problem of frequent itemsets under MIS (Multiple Item Support) thresholds and introduces Dynamic MIS algorithm. It is i) tree based and scans the database once, ii) considers multiple support thresholds, and iii) handles increments of additions, additions with new items and deletions. Proposed algorithm is compared to CFP-Growth++ and findings are; in dynamic database 1) Dynamic MIS performs better than CFP-Growth++ since it runs only on increments and 2) Dynamic MIS can achieve speed-up up to 56 times against CFP-Growth++.Conference Object Citation - WoS: 3Citation - Scopus: 6Spl-At Gherkin: a Gherkin Extension for Feature Oriented Testing of Software Product Lines(IEEE, 2019) Tuğlular, Tuğkan; Şensülün, SecanAs cloud platforms turn into software product lines (SPLs), testing products composed of customer selected features becomes more and more important. In this paper, we propose a feature-oriented testing approach for platform-based SPLs through a novel extension to Gherkin called SPL-AT Gherkin and a novel automatic test method generation technique, which utilizes TestNG framework. We demonstrate the applicability of the proposed approach by a case study.Conference Object Citation - WoS: 5Citation - Scopus: 5Featured Event Sequence Graphs for Model-Based Incremental Testing of Software Product Lines(IEEE, 2019) Tuğlular, Tuğkan; Beyazıt, Mutlu; Öztürk, DilekThe goal of software product lines (SPLs) is rapid development of high-quality software products in a specific domain with cost minimization. To assure quality of software products from SPLs, products need to be tested systematically. However, testing every product variant in isolation is generally not feasible for large number of product variants. An approach to deal with this issue is to use incremental testing, where test artifacts that are developed for one product are reused for another product which can be obtained by incrementally adding features to the prior product. We propose a novel model-based test generation approach for products developed using SPL that follows incremental testing paradigm. First, we introduce Featured Event Sequence Graphs (FESGs), an extension of ESGs, that provide necessary definitions and operations to support commonalities and variabilities in SPLs with respect to test models. Then we propose a test generation technique for the product variants of an SPL, which starts from any product. The proposed technique with FESGs avoids redundant test generation for each product from SPL. We compare our technique with in-isolation testing approach by a case study.Article Citation - WoS: 4Citation - Scopus: 5Generating Ontologies From Relational Data With Fuzzy-Syllogistic Reasoning(Springer Verlag, 2015) Kumova, Bora İsmailExisting standards for crisp description logics facilitate information exchange between systems that reason with crisp ontologies. Applications with probabilistic or possibilistic extensions of ontologies and reasoners promise to capture more information, because they can deal with more uncertainties or vagueness of information. However, since there are no standards for either extension, information exchange between such applications is not generic. Fuzzy-syllogistic reasoning with the fuzzy-syllogistic system4S provides 2048 possible fuzzy inference schema for every possible triple concept relationship of an ontology. Since the inference schema are the result of all possible set-theoretic relationships between three sets with three out of 8 possible fuzzy-quantifiers, the whole set of 2048 possible fuzzy inferences can be used as one generic fuzzy reasoner for quantified ontologies. In that sense, a fuzzy syllogistic reasoner can be employed as a generic reasoner that combines possibilistic inferencing with probabilistic ontologies, thus facilitating knowledge exchange between ontology applications of different domains as well as information fusion over them.Conference Object Citation - WoS: 5Citation - Scopus: 10An Exploratory Study on Usage of Process Mining in Agile Software Development(Springer Verlag, 2017) Erdem, Sezen; Demirörs, OnurAgile software development methods have become popular in the software development field during the last decade. Majority of software organizations develop or claim to develop software based on agile methods. Process mining is a process management technique that allows for the analysis of business processes based on the event logs. The aim of process mining is to discover, monitor and improve real processes, but not assumed processes, by extracting knowledge from event logs readily available in information systems. Process mining can be used to discover agile processes followed in organizations/projects to determine the actual processes followed. Process mining can also establish the necessary evidences for assessing or measuring the agility of organizations. This study explores the usability of process mining methods in agile software development context. The results of an exploratory case study on using process mining techniques in a software project managed by Scrum are depicted. We also discuss the benefits of the process mining techniques used and compare different tools utilized.Conference Object Citation - WoS: 1Citation - Scopus: 5Automation Architecture for Bayesian Network Based Test Case Prioritization and Execution(Institute of Electrical and Electronics Engineers Inc., 2016) Ufuktepe, Ekincan; Tuğlular, TuğkanAn automation architecture for Bayesian Network based test case prioritization is designed for software written in Java programming language following the approach proposed by Mirarab and Tahvildari [2]. The architecture is implemented as an integration of a series of tools and called Bayesian Network based test case prioritization and execution platform. The platform is triggered by a change in the source code, then it collects necessary information to be supplied to Bayesian Network and uses Bayesian Network evaluation results to run high priority unit tests.Book Part Citation - WoS: 299Citation - Scopus: 406Introduction To Machine Learning(Humana Press, 2014) Baştanlar, Yalın; Özuysal, MustafaThe machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.Article Citation - Scopus: 1Assuring Dependability of Software Reuse: an Industrial Standard(Springer Verlag, 2014) Belli, FevziWhereas a software component may be perfectly suited to one application, it may prove to cause severe faults in other applications. The prestandard IEC/PAS 62814 (Dependability of Software Products Containing Reusable Components – Guidance for Functionality and Tests), which has recently been released, addresses the functionality, testing, and dependability of software components to be reused and products that contain software to be used in more than one application; that is, reused by the same or by another development organization, regardless of whether it belongs to the same or another legal entity than the one that has developed this software. This paper introduces into this pre-standard and give hints how to use it. The author, who chaired its realization that started in 2006, briefly summarizes the difficult process to bring the industrial partners with controversial interests to a consensus.