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

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

Development Of Interaction Test Data Generation Strategy With Input-Output Mapping Supports

Uniform strength t-way testing (where t represents interaction strength) forms the basis of interaction testing. However, t is rarely uniform in real world as not all interaction faults are solely constituted by these fixed t-interactions. Consequently, a general solution has been introduced: input-output based relationship interaction testing. Although useful, most existing strategy implementations are lacking in terms of the automated input-output mapping support (to translate the symbolic outputs back into actual data form) and test suite generation flexibility. In order to address these aforementioned issues, a non-deterministic input-output based relationship interaction testing strategy, AURA, has been developed. AURA strategy also integrated with post-processing automated input-output mapping support and flexible iteration control capability to support test suite generation flexibility. Experimental results indicated that AURA strategy is generating competitive test suite size against existing strategies (Density, ParaOrder, Union, TVG, PICT, AETG, ACA, GA-N, IPO-N, IPO, Jenny, SA and ACS). Specifically, this strategy is capable to generate the test suite size as optimized as other strategies for certain inputs. Lastly, the post-processing automated input-output mapping support and flexible iteration control capability are evaluated with experiments

Model of human collective decision-making in complex environments

A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, consisting in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by two different forces: (i) the self-interest, which pushes them to increase their own fitness values, and (ii) the social interactions, which push individuals to reduce the diversity of their opinions in order to reach consensus. Results show that the performance of the group is strongly affected by the strength of social interactions and by the level of knowledge of the individuals. Increasing the strength of social interactions improves the performance of the team. However, too strong social interactions slow down the search of the optimal solution and worsen the performance of the group. In particular, we find that the threshold value of the social interaction strength, which leads to the emergence of a superior intelligence of the group, is just the critical threshold at which the consensus among the members sets in. We also prove that a moderate level of knowledge is already enough to guarantee high performance of the group in making decisions.Comment: 12 pages, 8 figues in European Physical Journal B, 201

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

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