Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Avoidance of Feature Configuration Faults in Software Product Lines(IEEE Computer Soc, 2025) Ergun, Burcu; Tuglular, Tugkan; Belli, FevziThis paper presents a validation approach to feature selection in software product lines (SPL). SPLs consist of similar products tailored to different needs, while SPLs sharing a common platform where feature configurations define product families. Validating feature configurations is critical to avoid defective shipments, recalls, and disposal. Exhaustive, pairwise, and combinatorial testing, among others, aim at ensuring configuration correctness. This paper introduces a novel method for improving feature selection and validation in SPLs by minimizing redundancy while ensuring configurations align with customer needs. The method emphasizes uncovering the differences in feature structures through "complex" and "simple" models, which helps identify and helps identify and tolerate potential errors arising from incorrect feature configurations. This ensures broader coverage while effectively managing dependencies. A case study using the Access Point (AP) SPL model, which is a networking device designed to enhance the strength of an existing wireless signal and expand its coverage area. The AP can enable or disable specific features on AP SPL depending on the characteristics of the third-party gateway with which it is integrated. AP SPL model with 66 features lead to 266 configurations, generated by Exhaustive Testing. Pairwise testing achieves 87% coverage with 132 test cases, while combinatorial testing reaches 94% with 45,760 cases. Our method ensures 100% feature coverage with just 3 test configurations. Thus, the approach introduced in this paper enhances product quality while reducing costs by avoiding redundant tests, making the approaches valuable for large-scale SPLs.Conference Object Citation - WoS: 1Konteyner Görüntülerini Kullanarak Hasar Tespiti ve Sınıflandırması(IEEE, 2020) Imamoglu, Zeynep Ekici; Tuglular, Tugkan; Bastanlar, YalinIn the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.Article Citation - WoS: 1Citation - Scopus: 3Automatic Test Sequence Generation and Functional Coverage Measurement From Uml Sequence Diagrams(Igi Global, 2023) Ekici, Nazim Umut; Tuglular, TugkanSequence diagrams define functional requirements through use cases. However, their visual form limits their usability in the later stages of the development life cycle. This work proposes a method to transform sequence diagrams into graph-based event sequence graphs, allowing the application of graph analysis methods and defining graph-based coverage criteria. This work explores these newfound abilities in two directions. The first is to use coverage criteria along with existing tests to measure their coverage levels, providing a metric of how well they address the scenarios defined in sequence diagrams. The second is to use coverage criteria to automatically generate effective and efficient acceptance test cases based on the scenarios defined in sequence diagrams. The transformation method is validated with over eighty non-trivial projects. The complete method is validated through a non-trivial example. The results show that the test cases generated with the proposed method are more effective at exposing faults and more efficient in test input size than user-generated test cases.