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    VISOR: A fast image processing pipeline with scaling and translation invariance for test oracle automation of visual output systems
    (The ACM Digital Library, 2018-02) Kıraç, Mustafa Furkan; Aktemur, Tankut Barış; Sözer, Hasan; Computer Science; KIRAÇ, Mustafa Furkan; AKTEMUR, Tankut Bariş; SÖZER, Hasan
    A test oracle automation approach proposed for systems that produce visual output.Root causes of accuracy issues analyzed for test oracles based on image comparison.Image processing techniques employed to improve the accuracy of test oracles.A fast image processing pipeline developed as an automated test oracle.An industrial case study performed for automated regression testing of Digital TVs. Test oracles differentiate between the correct and incorrect system behavior. Hence, test oracle automation is essential to achieve overall test automation. Otherwise, testers have to manually check the system behavior for all test cases. A common test oracle automation approach for testing systems with visual output is based on exact matching between a snapshot of the observed output and a previously taken reference image. However, images can be subject to scaling and translation variations. These variations lead to a high number of false positives, where an error is reported due to a mismatch between the compared images although an error does not exist. To address this problem, we introduce an automated test oracle, named VISOR, that employs a fast image processing pipeline. This pipeline includes a series of image filters that align the compared images and remove noise to eliminate differences caused by scaling and translation. We evaluated our approach in the context of an industrial case study for regression testing of Digital TVs. Results show that VISOR can avoid 90% of false positive cases after training the system for 4h. Following this one-time training, VISOR can compare thousands of image pairs within seconds on a laptop computer.
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    Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
    (The ACM Digital Library, 2019-06) Kıraç, Mustafa Furkan; Aktemur, Tankut Barış; Sözer, Hasan; Gebizli, C. Ş.; Computer Science; KIRAÇ, Mustafa Furkan; AKTEMUR, Tankut Bariş; SÖZER, Hasan
    We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model together with manually-applied test cases for robustness testing. We use this combination to train a long short-term memory (LSTM) network. As a result, the network learns an implicit representation of the usage behavior that is liable to failures. We use this network to generate new event sequences as test cases. We applied our approach in the context of an industrial case study for the black-box testing of a digital TV system. LSTM-generated test cases were able to reveal several faults, including critical ones, that were not detected with existing automated or manual testing activities. Our approach is complementary to model-based and exploratory testing, and the combined approach outperforms random testing in terms of both fault coverage and execution time.
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    ADVISOR: An adjustable framework for test oracle automation of visual output systems
    (IEEE, 2020-09) Genç, A. E.; Sözer, Hasan; Kıraç, Mustafa Furkan; Aktemur, Tankut Barış; Computer Science; SÖZER, Hasan; KIRAÇ, Mustafa Furkan; AKTEMUR, Tankut Bariş
    Test oracles differentiate between the correct and incorrect system behavior. Automation of test oracles for visual output systems mainly involves image comparison, where a snapshot of the output is compared with respect to a reference image. Hereby, the captured snapshot can be subject to variations such as scaling and shifting. These variations lead to incorrect evaluations. Existing approaches employ computer vision techniques to address a specific set of variations. In this article, we introduce ADVISOR, an adjustable framework for test oracle automation of visual output systems. It allows the use of a flexible combination and configuration of computer vision techniques. We evaluated a set of valid configurations with a benchmark dataset collected during the tests of commercial digital TV systems. Some of these configurations achieved up to 3% better overall accuracy with respect to state-of-the-art tools. Further, we observed that there is no configuration that reaches the best accuracy for all types of image variations. We also empirically investigated the impact of significant parameters. One of them is a threshold regarding image matching score that determines the final verdict. This parameter is automatically tuned by offline training. We evaluated runtime performance as well. Results showed that differences among the ADVISOR configurations and state-of-the-art tools are in the order of seconds per image comparison.
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    Fault masking as a service
    (Wiley, 2014-07) Gülcü, Koray; Sözer, Hasan; Aktemur, Tankut Barış; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; SÖZER, Hasan; AKTEMUR, Tankut Bariş; ERCAN, Ali Özer; Gülcü, Koray
    In SOA, composite services depend on a set of partner services to perform their tasks. These partner services may become unavailable because of system and/or network faults, leading to an increased error rate for the composite service. In this paper, we propose an approach to prevent the occurrence of errors that result from the unavailability of partner services. We introduce an external Web service, dubbed Fault Avoidance Service (FAS), to which composite services can register at will. After registration, FAS periodically checks the partner links, detects unavailable partner services, and updates the composite service with available alternatives. Thus, in case of a partner service error, the composite service will have been updated before attempting an ill-destined request. We provide mathematical analysis regarding the error rate and the false positive rate with respect to the monitoring frequency of FAS for two models. We obtained empirical results by conducting several tests on the Amazon Elastic Compute Cloud to evaluate our mathematical analyses. We also introduce an industrial case study for improving the quality of a service-oriented system from the broadcasting and content delivery domain.
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    Rumadai: A plug-in to record and replay client-side events of web sites with dynamic content
    (IEEE, 2012) Yıldız, Asım; Aktemur, Tankut Barış; Sözer, Hasan; Computer Science; AKTEMUR, Tankut Bariş; SÖZER, Hasan; Yıldız, Asım
    Reproducing user events when testing web pages is challenging because of the dynamic nature of the web content and potential dependency on third party content providers. We present Rumadai, a Visual Studio plug-in, that helps web programmers test web pages by recording and replaying clientside events. Rumadai injects code into web pages to be deployed at servers. The injected code, written in JavaScript, records user events as well as client-side dynamic content requests (e.g. via Ajax or Jsonp) and their responses. Recorded events and responses are then sent to a remote database via HTTP POST. Web page developers can query the saved client data, again using Rumadai seamlessly from Visual Studio, to replay all or a subset of events on a browser.
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    Towards subtyped program generation in F#
    (ACM, 2011) Aktemur, Tankut Barış; Computer Science; AKTEMUR, Tankut Bariş
    Program Generation is the technique of combining code fragments to construct a program. In this work we report on our progress to extend F# with program generation constructs. Our prototype implementation uses a translation that allows simulating program generators by regular programs. The translation enables fast implementation and experimentation. We state how a further extension with subtyping can be integrated by benefiting from the translation.
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    Adaptive domain-specific service monitoring
    (Springer Science+Business Media, 2014) Ünsal, A. A.; Sazara, G.; Aktemur, Tankut Barış; Sözer, Hasan; Computer Science; AKTEMUR, Tankut Bariş; SÖZER, Hasan
    We propose an adaptive and domain-specific service monitoring approach to detect partner service errors in a cost-effective manner. Hereby, we not only consider generic errors such as file not found or connection timed out, but also take domain-specific errors into account. The detection of each type of error entails a different monitoring cost in terms of the consumed resources. To reduce costs, we adapt the monitoring frequency for each service and for each type of error based on the measured error rates and a cost model. We introduce an industrial case study from the broadcasting and content-delivery domain for improving the user-perceived reliability of Smart TV systems. We demonstrate the effectiveness of our approach with real data collected to be relevant for a commercial TV portal application. We present empirical results regarding the trade-off between monitoring overhead and error detection accuracy. Our results show that each service is usually subject to various types of errors with different error rates and exploiting this variation can reduce monitoring costs by up to 30% with negligible compromise on the quality of monitoring.
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    A sparse matrix‐vector multiplication method with low preprocessing cost
    (Wiley, 2018-11-10) Aktemur, Tankut Barış; Computer Science; AKTEMUR, Tankut Bariş
    Sparse matrix-vector multiplication (SpMV) is a crucial operation used for solving many engineering and scientific problems. In general, there is no single SpMV method that gives high performance for all sparse matrices. Even though there exist sparse matrix storage formats and SpMV implementations that yield high efficiency for certain matrix structures, using these methods may entail high preprocessing or format conversion costs. In this work, we present a new SpMV implementation, named CSRLenGoto, that can be utilized by preprocessing the Compressed Sparse Row (CSR) format of a matrix. This preprocessing phase is inexpensive enough for the associated cost to be compensated in just a few repetitions of the SpMV operation. CSRLenGoto is based on complete loop unrolling and gives performance improvements in particular for matrices whose mean row length is low. We parallelized our method by integrating it into a state-of-the-art matrix partitioning approach as the kernel operation. We observed up to 2.46× and on the average 1.29× speedup with respect to Intel MKL's SpMV function for matrices with short- or medium-length rows.
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    FAS: introducing a service for avoiding faults in composite services
    (Springer Science+Business Media, 2012) Gülcü, K.; Sözer, Hasan; Aktemur, Tankut Barış; Computer Science; SÖZER, Hasan; AKTEMUR, Tankut Bariş
    In service-oriented architectures, composite services depend on a set of partner services to perform the required tasks. These partner services may become unavailable due to system and/or network faults, leading to an increased error rate for the composite service. In this paper, we propose an approach to prevent the occurrence of errors that result from the unavailability of partner services. We introduce an external Web service, FAS (Fault Avoidance Service), to which composite services can register at will. After registration, FAS periodically checks the partner links, detects unavailable partner services, and updates the composite service with available alternatives. Thus, in case of a partner service error, the composite service will have been updated before invoking the partner service. We provide mathematical analysis regarding the error rate and the ratio of false positives with respect to the monitoring frequency of FAS for different partner service availabilities. We also provide empirical results regarding these metrics based on several tests we performed using the Amazon Elastic Compute Cloud.
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    Effort estimation for architectural refactoring to introduce module isolation
    (Springer, 2014) Öztürk, F.; Sarılı, E.; Sözer, Hasan; Aktemur, Tankut Barış; Computer Science; SÖZER, Hasan; AKTEMUR, Tankut Bariş
    The decomposition of software architecture into modular units is driven by both functional and quality concerns. Dependability and security are among quality concerns that require a software to be decomposed into separate units isolated from each other. However, it appears that this decomposition is usually not aligned with the decomposition based on functional concerns. As a result, introducing module isolation forced by quality attributes, while preserving the existing decomposition, is not trivial and requires a substantial refactoring effort. In this work, we introduce an approach and a toolset to predict this effort prior to refactoring activities. As such, a selection can be made among potential decomposition alternatives based on quantitative estimations. These estimations are obtained from scalable analysis of module dependencies based on a graph database and reusable query templates. We discuss our experiences and evaluate our approach on a code base used in a commercial Digital TV and Set-top Box software.