Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testingComment: Electronic System Design (ISED), 2010 International Symposium on Issue Date: 20-22 Dec. 2010 On page(s): 235 - 240 Location: Bhubaneswar Print ISBN: 978-1-4244-8979-4 INSPEC Accession Number: 11835766 Digital Object Identifier: 10.1109/ISED.2010.5

Automated Software Testing Using Metahurestic Technique Based on Improved Ant Algorithms for Software Testing

Testing can never completely identify all the defects within software [1]. Instead, it furnishes a criticism or comparison that compares the state and behavior of the product against oracles principles or mechanisms by which someone might recognize a problem. These oracles may include (but are not limited to) specifications, contracts[2], comparable products, past versions of the same product, inferences about intended or expected purpose, user or customer expectations, relevant standards, applicable laws, or other criteria. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two Meta heuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing

Ant colony optimization for object-oriented unit test generation

Generating useful unit tests for object-oriented programs is difficult for traditional optimization methods. One not only needs to identify values to be used as inputs, but also synthesize a program which creates the required state in the program under test. Many existing Automated Test Generation (ATG) approaches combine search with performance-enhancing heuristics. We present Tiered Ant Colony Optimization (Taco) for generating unit tests for object-oriented programs. The algorithm is formed of three Tiers of ACO, each of which tackles a distinct task: goal prioritization, test program synthesis, and data generation for the synthesised program. Test program synthesis allows the creation of complex objects, and exploration of program state, which is the breakthrough that has allowed the successful application of ACO to object-oriented test generation. Taco brings the mature search ecosystem of ACO to bear on ATG for complex object-oriented programs, providing a viable alternative to current approaches. To demonstrate the effectiveness of Taco, we have developed a proof-of-concept tool which successfully generated tests for an average of 54% of the methods in 170 Java classes, a result competitive with industry standard Randoop

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

Integrasi Pareto Fitness, Multiple-Population Dan Temporary Population Pada Algoritma Genetika Untuk Pembangkitan Data Tes Pada Pengujian Perangkat Lunak

Pengujian perangkat lunak memerlukan biaya yang mahal dan sering kali lebih dari 50% biaya keseluruhan dalam pengembangan perangkat lunak digunakan dalam tahapan ini. Untuk mengurangi biaya proses pengujian perangkat lunak secara otomatis dapat digunakan. Hal yang sangat penting dalam pengujian perangkat lunak secara otomatis adalah proses menghasilkan data tes. Pengujian secara otomatis yang paling efektif dalam menekan biaya adalah pengujian branch coverage. Salah satu metode yang banyak digunakan dan memiliki kinerja baik adalah algoritma genetika (AG). Salah satu permasalahan AG dalam menghasilkan data tes adalah ketiga target cabang dipilih memungkinkan tidak ada satupun individu yang memenuhi kriteria. Hal ini akan menyebabkan proses pencarian data tes memakan waktu lebih lama. Oleh karena itu di dalam penelitian ini diusulkan integrasi pareto fitness, multiple-population dan temporary population di dalam proses pencarian data tes dengan menggunakan AG (AG-PFMPTP). Multiple-population diusulkan untuk menghindari premature convergence. Kemudian pareto fitness dan temporary population digunakan untuk mencari beberapa data tes sekaligus, kemudian mengevaluasinya dan memasukkan ke dalam archive temporary population. Dari hasil pengujian yang telah dilakukan rata-rata generasi metode AG-PFMPTP secara signifikan lebih sedikit dalam menghasilkan data tes yang dibutuhkan dibandingkan metode AG standar ataupun AG dengan multiple-population (AG-MP) pada semua benchmark program yang digunakan. Hal tersebut menunjukkan metode yang diusulkan lebih cepat dalam mencari data tes yang dibutuhka

Ant colony optimization for object-oriented unit test generation

Generating useful unit tests for object-oriented programs is difficult for traditional optimization methods. One not only needs to identify values to be used as inputs, but also synthesize a program which creates the required state in the program under test. Many existing Automated Test Generation (ATG) approaches combine search with performance-enhancing heuristics. We present Tiered Ant Colony Optimization (Taco) for generating unit tests for object-oriented programs. The algorithm is formed of three Tiers of ACO, each of which tackles a distinct task: goal prioritization, test program synthesis, and data generation for the synthesised program. Test program synthesis allows the creation of complex objects, and exploration of program state, which is the breakthrough that has allowed the successful application of ACO to object-oriented test generation. Taco brings the mature search ecosystem of ACO to bear on ATG for complex object-oriented programs, providing a viable alternative to current approaches. To demonstrate the effectiveness of Taco, we have developed a proof-of-concept tool which successfully generated tests for an average of 54% of the methods in 170 Java classes, a result competitive with industry standard Randoop

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

A test case generation framework based on UML statechart diagram

Early software fault detection offers more flexibility to correct errors in the early development stages. Unfortunately, existing studies in this domain are not sufficiently comprehensive in describing the major processes of the automated test case generation. Furthermore, the algorithms used for test case generation are not provided or well described. Current studies also hardly address loops and parallel paths issues, and achieved low coverage criteria. Therefore, this study proposes a test case generation framework that generates minimized and prioritized test cases from UML statechart diagram with higher coverage criteria. This study, conducted a review of the previous research to identify the issues and gaps related to test case generation, model-based testing, and coverage criteria. The proposed framework was designed from the gathered information based on the reviews and consists of eight components that represent a comprehensive test case generation processes. They are relation table, relation graph, consistency checking, test path minimization, test path prioritization, path pruning, test path generation, and test case generation. In addition, a prototype to implement the framework was developed. The evaluation of the framework was conducted in three phases: prototyping, comparison with previous studies, and expert review. The results reveal that the most suitable coverage criteria for UML statechart diagram are all-states coverage, all-transitions coverage, alltransition-pairs coverage, and all-loop-free-paths coverage. Furthermore, this study achieves higher coverage criteria in all coverage criteria, except for all-state coverage, when compared with the previous studies. The results of the experts’ review show that the framework is practical, easy to implement due to it is suitability to generate the test cases. The proposed algorithms provide correct results, and the prototype is able to generate test case effectively. Generally, the proposed system is well accepted by experts owing to its usefulness, usability, and accuracy. This study contributes to both theory and practice by providing an early alternative test case generation framework that achieves high coverage and can effectively generate test cases from UML statechart diagrams. This research adds new knowledge to the software testing field, especially for testing processes in the model-based techniques, testing activity, and testing tool support

Penggunaan ant colony optimization untuk pembangkitan data tes secara otomatis dalam pengujian perangkat lunak

INDONESIA : Pengujian perangkat lunak memainkan peran yang penting dalam menjaga kualitas perangkat lunak. Akan tetapi, pengujian memakan cukup banyak biaya, waktu, dan tenaga. Bahkan, sering kali biaya untuk pengujian perangkat lunak lebih dari 50% dari total biaya dalam pengembangan perangkat lunak. Selain itu, Menghasilkan data tes merupakan langkah terpanjang dalam proses pengujian perangkat lunak. Oleh karena itu, pembuatan data uji dan evaluasi pengujian adalah pekerjaan yang paling menuntut dan akan sangat diuntungkan dari otomatisasi. Untuk mendapatkan hasil dengan akurasi yang baik diterapkan pendekatan kecerdasan buatan. Pada penelitian ini diusulkan algoritma Ant Colony Optimization (ACO) sebagai pembangkit data uji untuk menjalankan semua cabang suatu program. Control flow graph dibangkitkan dari sebuah kode program untuk menggambarkan aliran kode program tersebut. Kemudian, data tes yang sesuai untuk setiap jalur percabangan dihasilkan. Dengan adanya data tes yang akurat, maka kesalahan-kesalahan pada perangkat lunak yang diuji dapat segera diketahui. ENGLISH : Software testing plays an important role in maintaining software quality. However, testing costs a lot of money, time and effort. In fact, often the cost of software testing is more than 50% of the total cost of software development. In addition, Generating test data is the longest step in the software testing process. Therefore, test data generation and test evaluation are the most demanding jobs and will greatly benefit from automation. To get results with good accuracy, an artificial intelligence approach is applied. In this study, the Ant Colony Optimization (ACO) algorithm is proposed as a test data generator to run all branches of a program. The control flow graph is generated from a program code to describe the flow of the program code. Then, the appropriate test data for each branching path is generated. With accurate test data, errors in the software being tested can be immediately identified. ARABIC : يلعب اختبار البرامج دورًا مهمًا في الحفاظ على جودة البرامج. ومع ذلك ، فإن الاختبار يتطلب الكثير من الوقت والمال. في الواقع ، غالبًا ما يتطلب أكثر من ۰۵٪ من التكلفة الإجمالية لتطوير البرمجيات. يعد إنشاء بيانات الاختبار المرحلة الأكثر استهلاكا للوقت والأكثر استهلاكا للتكلفة في اختبار البرامج. وبالتالي ، فإن إنشاء بيانات الاختبار وتقييم الاختبار هما الأكثر تطلبًا وسيستفيدان بشكل كبير من الأتمتة. من أجل الحصول على نتائج بدقة جيدة ، يتم تطبيق نهج ذكاء اصطناعي. في هذه الدراسة ، تم اقتراح خوارزمية تحسين مستعمرة النمل (Ant Colony Optimization (ACO)) كمولد بيانات اختبار لتنفيذ جميع الفروع في البرنامج. يتم إنشاء الرسم البياني للتحكم في التدفق من رمز برنامج لوصف تدفق رمز البرنامج. بعد ذلك ، بيانات الاختبار المناسبة لكل مسار متفرع. باستخدام بيانات الاختبار الدقيقة ، يمكن العثور على أخطاء البرامج على الفور

Reducing Software Testing Time with Combinatorial Testing and Test Automation

The development of large software systems is a complex and error prone process. Errors may occur at any stage of software development. These errors, sometimes referred to as bugs, can cause great losses in terms of both time and money if not identified and removed as early as possible. Testing a software product is costly, since it takes much time and need to test many combinations of its functions, integrity, performance etc., which can be called as test cases. The company\u27s goal is to reduce the testing time, so that they can save money and deliver the product much faster to the customer. Testing time can be reduced in two main ways, first by reducing number of test cases and second by automating repeatedly testing areas. This paper will discuss fundamentals of testing such as importance and difference of verification and validation, testing throughout the software development life cycle and testing methods, levels and types. Then it will discuss the possibility of reducing the time spent on testing by reducing number of test cases with combinatorial testing and automating repeatedly tested areas with test automation using Selenium tool. Finally it will also shed some light on a real world test automation project with selenium and two integrated develop environments