Effectiveness Assessment of the Search-Based Statistical Structural Testing

Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting ability. This article tries to answer the following question: if we use a relaxed constraint that significantly reduces the time consumption on search, can the optimized input distribution still be effective in fault-detecting ability? In this article, we propose a type of criterion called fairness-enhanced-sum-of-triggering-probability (p-L1-Max). The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data generation. We conducted extensive experiments to compare the computation time and the fault-detecting ability between the two criteria. The result shows that the 1.0-L1-Max criterion has the highest efficiency, and it is more practical to use than the Tri-Low-Bound criterion. To measure a criterion’s fault-detecting ability, we introduce a definition of expected faults found in the effective test set size region. To measure the effective test set size region, we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition

An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

حظي تقييم البرمجة التلقائي (APA) بالكثير من الاهتمام بين الباحثين بشكل أساسي لدعم الدرجات الآلية ووضع علامات على المهامالمكلف بادائها الطلاب أو التدريبات بشكل منهجي. يتم تعريف APA بشكل شائع كطريقة يمكن أن تعزز الدقة والكفاءة والاتساق وكذلك تقديمملاحظات فورية لحلول للطلاب. في تحقيق APA ، تعد عملية إنشاء بيانات الاختبار مهمة للغاية وذلك لإجراء اختبار ديناميكي لمهمةالطلاب. في مجال اختبار البرمجيات ، أوضحت العديد من الأبحاث التي تركز على توليد بيانات الاختبار نجاح اعتماد تقنيات البحث الفوقية(MHST) من أجل تعزيز إجراءات استنباط بيانات الاختبار المناسبة للاختبار الفعال. ومع ذلك، فإن الأبحاث التي أجريت على APA حتىالآن لم تستغل بعد التقنيات المفيدة لتشمل تغطية اختبار جودة برنامج أفضل. لذلك ، أجرت هذه الدراسة تقييماً مقارنا لتحديد أي تقنية بحثفوقي قابلة للتطبيق لدعم كفاءة توليد بيانات الاختبار الآلي (ATDG) في تنفيذ اختبار وظيفي ديناميكي. في تقييم البرمجة التلقائي يتم تضمينالعديد من تقنيات البحث الفوقية الحديثة في التقييم المقارن الذي يجمع بين كل من خوارزميات البحث المحلية والعالمية من عام 2000 حتىعام 2018 .تشير نتيجة هذه الدراسة إلى أن تهجين Cuckoo Search مع Tabu Search و lévy flight كواحدة من طرق البحث الفوقية الواعدةليتم تطبيقها ، حيث أنه يتفوق على الطرق الفوقية الأخرى فيما يتعلق بعدد التكرارات ونطاق المدخلات.Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs

An analysis on the applicability of meta-heuristic searching techniques for automated test data generation in automatic programming assessment

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs

Genetic algorithm based test data generator

Abstract- Effective and efficient test data generation is one of the major challenging and time-consuming tasks within the software testing process. Researchers have proposed different methods to generate test data automatically, however, those methods suffer from different drawbacks. In this paper we present a Genetic Algorithm-based approach that tries to generate a set of test data that is expected to cover a given set of target paths. Our proposed fitness function is intended to achieve path coverage that incorporates path traversal techniques, neighborhood influence, weighting, and normalization. This integration improves the GA performance in terms of search space exploitation and exploration, and allows faster convergence. We performed some experiments using our proposed approach, where results were promising.