A Fitness Function for Search-based Testing of Java Classes, which is Based on the States Reached by the Object under Test

Genetic Algorithms are among the most efficient search-based techniques to automatically generate unit test cases today. The search is guided by a fitness function which evaluates how close an individual is to satisfy a given coverage goal. There exists several coverage criteria but the default criterion today is branch coverage. Nevertheless achieving high or full branch coverage does not imply that the generated test suite has good quality. In object oriented programs the state of the object affects its behavior. Thereupon, test cases that put the object under test, in new states are of interest in the testing context. In this article we propose a new fitness function which takes into consideration three factors for evaluation: the approach level, the branch distance and the new states reached by a test case. The coverage targets are still the branches, but during the search, the state of the object under test evolves with the scope to produce individuals that discover interesting features of the class and as a consequence can discover errors. We implemented this fitness function in the eToc tool. In our experiments the usage of the proposed fitness function towards the original fitness function results in a relative increase of 15.6% in the achieved average mutation score with the cost of a relative increase of 12.6% in the average test suite size

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

Observations in using parallel and sequential evolutionary algorithms for automatic software testing

Computers & Operations Research, 35 (10),2007, pp.3161-3183In this paper we analyze the application of parallel and sequential evolutionary algorithms (EAs) to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential EAs have been used in the past for this task. We explore here the use of parallel EAs. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.Ministry of Education and Science and FEDER under Contract TIN2005-08818-C04-01 (the OPLINK Project). Francisco Chicano was supported by a Grant (BOJA 68/2003) from the Junta de Andalucía (Spain)

An Overview of Recent Trends in Software Testing

In the field of search based software testing, genetic algorithm based testing has received a major share of attention among researchers during the last few years. Though there are advantages for this type of testing, there also exist some practical difficulties which can make this technique less attractive for software testing industry. The potential of program slicing in testing has not been fully exploited till now and the works that have explicitly demonstrated the application of slicing in testing field are rare. Our paper aims to analyze existing techniques for software testing and to introduce an approach for software testing using program slicing technique. A systematic review of genetic algorithm based works reveals that, fitness function design, population initialization and parameter settings impact the quality of solution obtained in software testing using genetic algorithm. Based on the conclusions from the existing literature, we have probed deeper about the issues in these areas. Making an unbiased review like this may help to solve these unresolved issues in genetic algorithm based software testing. In this work, we have emphasized and has given clear directions on how slicing can be used as a potential tool for practical software testing. In addition, a set of research questions have been framed, which may be answered by reviewing the study made in this work. This may help future research in this area, leading to major breakthrough in software testing field

Improving the reliability and validity of test data adequacy in programming assessments

Automatic Programming Assessment (or APA) has recently become a notable method in assisting educators of programming courses to automatically assess and grade students’ programming exercises as its counterpart; the typical manual tasks are prone to errors and lead to inconsistency. Practically, this method also provides an alternative means of reducing the educators’ workload effectively. By default, test data generation process plays an important role to perform a dynamic testing on students’ programs. Dynamic testing involves the execution of a program against different inputs or test data and the comparison of the results with the expected output, which must conform to the program specifications. In the software testing field, there have been diverse automated methods for test data generation. Unfortunately, APA rarely adopts these methods. Limited studies have attempted to integrate APA and test data generation to include more useful features and to provide a precise and thorough quality program testing. Thus, we propose a framework of test data generation known as FaSt-Gen covering both the functional and structural testing of a program for APA. Functional testing is a testing that relies on specified functional requirements and focuses the output generated in response to the selected test data and execution, Meanwhile, structural testing looks at the specific program logic to verify how it works. Overall, FaSt-Gen contributes as a means to educators of programming courses to furnish an adequate set of test data to assess students’ programming solutions regardless of having the optimal expertise in the particular knowledge of test cases design. FaSt-Gen integrates the positive and negative testing criteria or so-called reliable and valid test adequacy criteria to derive desired test data and test set schema. As for the functional testing, the integration of specification-derived test and simplified boundary value analysis techniques covering both the criteria. Path coverage criterion guides the test data selection for structural testing. The findings from the conducted controlled experiment and comparative study evaluation show that FaSt-Gen improves the reliability and validity of test data adequacy in programming assessments

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