A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al

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

Interactive ant colony optimization (iACO) for early lifecycle software design

Finding good designs in the early stages of the software development lifecycle is a demanding multi-objective problem that is crucial to success. Previously, both interactive and non-interactive techniques based on evolutionary algorithms (EAs) have been successfully applied to assist the designer. However, recently ant colony optimization was shown to outperform EAs at optimising quantitative measures of software designs with a limited computational budget. In this paper, we propose a novel interactive ACO (iACO) approach, in which the search is steered jointly by an adaptive model that combines subjective and objective measures. Results show that iACO is speedy, responsive and effective in enabling interactive, dynamic multi-objective search. Indeed, study participants rate the iACO search experience as compelling. Moreover, inspection of the learned model facilitates understanding of factors affecting users' judgements, such as the interplay between a design's elegance and the interdependencies between its components. © 2014 Springer Science+Business Media New York

Artificial table testing dynamically adaptive systems

Dynamically Adaptive Systems (DAS) are systems that modify their behavior and structure in response to changes in their surrounding environment. Critical mission systems increasingly incorporate adaptation and response to the environment; examples include disaster relief and space exploration systems. These systems can be decomposed in two parts: the adaptation policy that specifies how the system must react according to the environmental changes and the set of possible variants to reconfigure the system. A major challenge for testing these systems is the combinatorial explosions of variants and envi-ronment conditions to which the system must react. In this paper we focus on testing the adaption policy and propose a strategy for the selection of envi-ronmental variations that can reveal faults in the policy. Artificial Shaking Table Testing (ASTT) is a strategy inspired by shaking table testing (STT), a technique widely used in civil engineering to evaluate building's structural re-sistance to seismic events. ASTT makes use of artificial earthquakes that simu-late violent changes in the environmental conditions and stresses the system adaptation capability. We model the generation of artificial earthquakes as a search problem in which the goal is to optimize different types of envi-ronmental variations

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

An ant colony based model to optimize parameters in industrial vision

Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful

A review paper: optimal test cases for regression testing using artificial intelligent techniques

The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all

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