2,229 research outputs found
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Adopting A Particle Swarm-Based Test Generator Strategy For Variable-Strength And T-Way Testing
Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based
algorithms as t-way (where t indicates the interaction strength) and variable-strength
testing strategies. Many AI-based strategies have been developed, such as Ant Colony,
Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most
existing AI-based strategies adopt complex search processes and require heavy
computations. For this reason, existing AI-based strategies have been confined to small
interaction strengths (i.e., t≤3) and small test configurations. Recent studies demonstrate
the need to go up to t=6 in order to capture most faults. This thesis presents the design
and implementation of a new interaction test generation strategy, known as the Particle
Swarm-based Test Generator (PSTG), for generating t-way and variable-strength test
suites. Unlike other existing AI-based strategies, the lightweight computation of the
particle swarm search process enables PSTG to support high interaction strengths of up to
t=6. The performance of PSTG is evaluated using several sets of benchmark experiments.
Comparatively, PSTG consistently outperforms its AI counterparts and other existing
strategies as far as the size of the test suite is concerned. Furthermore, the case study
demonstrates the usefulness of PSTG for detecting faulty interactions of the input
components
CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm
ويكاد يكون من المستحيل اختبار كل مجموعة من المدخلات نظرًا لأن تنفيذ حالات الاختبار يتطلب وقتا طويلا للغاية. الأختبار الاندماجي هو السبيل لتخطي عقبات الاختبار الشامل من خلال أختبار كل قيم المدخلات لكل المعاملات المركبة المتعددة طرق الترتيب. يمكن تقسيم الاختبار التجميعي إلى ثلاثة أنواع هي تفاعل القوة الموحد ، والتفاعل المتغير والقوة ، والعلاقة القائمة على المدخلات والمخرجات . ان الطريقة الاخيرة الانفة الذكر تختزل الفحص الاندماجي الى مجموعة ضمن اختيار الشخص الفاحص. معظم الابحاث في الاختبار الاندماجي طبقت في تفاعل القوة الموحدة وقوة التفاعل المتغيرة ، ومع ذلك ، هناك اهتمام قليل جدا بالعلاقة بين المدخلات والمخرجات. لذا تم اقتراح خوارزمية جايا في هذا البحث كخوارزمية مثلي لانشاء جدول الفحص الاندماجي باستراتيجية تعتمد على العلاقة بين المدخلات والمخرجات. نتيجة تطبيق خوارزمية جايا في الاختبار الاندماجي القائم على المدخلات والمخرجات مقبولة لأنها تنتج العدد الأمثل تقريبًا لحالات الاختبار في نطاق زمني مقبول.Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing applied the uniform and the variable interaction strength, however, there is still a lack of work addressing IOR. In this paper, a Jaya algorithm is proposed as an optimization algorithm engine to construct a test list based on IOR in the proposed combinatorial test list generator strategy into a tool called CTJ. The result of applying the Jaya algorithm in input-output based combinatorial testing is acceptable since it produces a nearly optimum number of test cases in a satisfactory time range
A review of artificial intelligence strategies in covering array construction
Software systems are getting larger in size and functionality. Exhaustive software testing is becoming nearly impossible with larger systems. Objective: Researchers are focusing on methods and strategies to optimize software testing process by applying computational based strategies as well as Artificial Intelligence (AI) based strategy. Results: This paper reviews the AI based strategies and its effectiveness in being solution for this optimization problem compared to computational based tools and strategies
HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation
Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution
GALP: A hybrid artificial intelligence algorithm for generating covering array
Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance
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