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.Article Citation - WoS: 9Citation - Scopus: 11Test Input Generation From Cause-Effect Graphs(Springer, 2021) Kavzak Ufuktepe, Deniz; Ayav, Tolga; Belli, FevziCause-effect graphing is a well-known requirement-based and systematic testing method with a heuristic approach. Since it was introduced by Myers in 1979, there have not been any sufficiently comprehensive studies to generate test inputs from these graphs. However, there exist several methods for test input generation from Boolean expressions. Cause-effect graphs can be more convenient for a wide variety of users compared to Boolean expressions. Moreover, they can be used to enforce common constraints and rules on the system variables of different expressions of the system. This study proposes a new mutant-based test input generation method, Spectral Testing for Boolean specification models based on spectral analysis of Boolean expressions using mutations of the original expression. Unlike Myers' method, Spectral Testing is an algorithmic and deterministic method, in which we model the possible faults systematically. Furthermore, the conversion of cause-effect graphs between Boolean expressions is explored so that the existing test input generation methods for Boolean expressions can be exploited for cause-effect graphing. A software is developed as an open-source extendable tool for generating test inputs from cause-effect graphs by using different methods and performing mutation analysis for quantitative evaluation on these methods for further analysis and comparison. Selected methods, MI, MAX-A, MUTP, MNFP, CUTPNFP, MUMCUT, Unique MC/DC, and Masking MC/DC are implemented together with Myers' technique and the proposed Spectral Testing in the developed tool. For mutation testing, 9 common fault types of Boolean expressions are modeled, implemented, and generated in the tool. An XML-based standard on top of GraphML representing a cause-effect graph is proposed and is used as the input type to the approach. An empirical study is performed by a case study on 5 different systems with various requirements, including the benchmark set from the TCAS-II system. Our results show that the proposed XML-based cause-effect graph model can be used to represent system requirements. The developed tool can be used for test input generation from proposed cause-effect graph models and can perform mutation analysis to distinguish between the methods with respect to the effectiveness of test inputs and their mutant kill scores. The proposed Spectral Testing method outperforms the state-of-the-art methods in the context of critical systems, regarding both the effectiveness and mutant kill scores of the generated test inputs, and increasing the chances of revealing faults in the system and reducing the cost of testing. Moreover, the proposed method can be used as a separate or complementary method to other well-performing test input generation methods for covering specific fault types.