Kamal Z. Zamli

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts.

MIPOG - An Efficient t-Way Minimization Strategy for Combinatorial Testing

This paper presents a study comparing different techniques to achieve minimal test suites in combinatorial testing. Considering high interaction strength is not without difficulties. When the number of parameter coverage increases, the size of t-way test sets also increases exponentially, hence, resulting into combinatorial explosion problem. Addressing these aforementioned issues, a new strategy capable of supporting high interaction strength, called Modified IPOG (MIPOG) is proposed. Similar to its predecessor IPOG (In Parameter Order General), MIPOG adopts the horizontal and vertical extensions in order to construct the desired test set. However, unlike IPOG, MIPOG optimizes both the horizontal and vertical extensions resulting into a smaller size solution than that of IPOG, (i.e., with the test size ratio ≤ 1). In fact, MIPOG, in most cases, surpasses some IPOG variants (IPOD, IPOF1, and IPOF2) as well as other existing strategies (Jenny, TVG, TConfig, and ITCH), as far as the test size is concerned with an acceptable execution time. Additionally, MIPOG has also contributed to enhance many known CA and MCA that exist in the literature.

Development of Java based RFID application programmable interface for heterogeneous RFID system

Developing RFID based applications is a painstakingly difficult endeavor. The difficulties include non-standard software and hardware peripherals from vendors, interoperability problems between different operating systems as well as lack of expertise in terms of low-level programming for RFID (i.e. steep learning curve). In order to address these difficulties, a reusable RFIDTM API (RFID Tracking & Monitoring Application Programmable Interface) for heterogeneous RFID system has been designed and implemented. The API has been successfully employed in a number of application prototypes including tracking of inventories as well as human/object tracking and tagging. Here, the module has been tested on a number of different types and configuration of active and passive readers including that LF and UHF Readers.

Constrained Interaction Testing: A Systematic Literature Study

Interaction testing can be used to effectively detect faults that are otherwise difficult to find by other testing techniques. However, in practice, the input configurations of software systems are subjected to constraints, especially in the case of highly configurable systems. Handling constraints effectively and efficiently in combinatorial interaction testing is a challenging problem. Nevertheless, researchers have attacked this challenge through different techniques, and much progress has been achieved in the past decade. Thus, it is useful to reflect on the current achievements and shortcomings and to identify potential areas of improvements. This paper presents the first comprehensive and systematic literature study to structure and categorize the research contributions for constrained interaction testing. Following the guidelines of conducting a literature study, the relevant data are extracted from a set of 103 research papers belonging to constrained interaction testing. The topics addressed in constrained interaction testing research are classified into four categories of constraint test generation, application, generation and application, and model validation studies. The papers within each of these categories are extensively reviewed. Apart from answering several other research questions, this paper also discusses the applications of constrained interaction testing in several domains, such as software product lines, fault detection and characterization, test selection, security, and graphical user interface testing. This paper ends with a discussion of limitations, challenges, and future work in the area.

A Parameter Free Choice Function Based Hyper-Heuristic Strategy for Pairwise Test Generation

Hyper-heuristics are advanced high-level search methodologies that solve hard computational problems indirectly via low-level heuristics. Choice function based hyper-heuristics are selection and acceptance hyper-heuristics that use statistical information to rank low-level heuristics for selection. In this paper, we describe a choice function based hyper-heuristic called Pairwise Choice Function based Hyper-heuristic (PCFHH) for the pairwise test generation problem. PCFHH uses a combination of three measures to select and apply an effective low-level heuristic from a set of four low-level heuristics at any stage of the search. Our experimental results have been encouraging as PCFHH outperforms most of pairwise test generation strategies on many of the problem instances.

Sequence and sequence-less T-way test suite generation strategy based on flower pollination algorithm

In an attempt to ensure good-quality software, there is need to test all possible inputs. Owing to the fact that the exhaustive testing is hardly feasible, many software testing approaches has been proposed. Combinatorial Interaction Testing (CIT) is very promising technique to minimize the number of test cases. Although useful, most of exiting CIT strategies and tools focus on data inputs and assume “sequence-less” interactions between input parameters. However, reactive systems show sequence related behaviors and their faults may not expose if the sequence of inputs are not considered. In this paper, we propose a new t-way strategy (i.e. t refers to the degree of the combination) strategy, called Flower Strategy (FS), that addresses both sequence and sequence-less test generation. Experimental results show that FS produces test size.

Assessing optimization based strategies for t-way test suite generation: The case for flower-based strategy

Exhaustive testing is extremely difficult to perform owing to the large number of combinations. Thus, sampling and finding the optimal test suite from a set of feasible test cases becomes a central concern. Addressing this issue, the adoption of t-way testing (where t indicates the interaction strength) has come into the limelight. In order to summarize the achievements so far and facilitate future development, the main focus of this paper is, first, to present a critical comparison of adoption optimization algorithms (OA) as a basis of the t-way test suite generation strategy and, second, to propose a new t-way strategy based on Flower Pollination Algorithm, called Flower Strategy (FS). Analytical and experimental results demonstrate the applicability of FS for t-way test suite generation.

An automated software fault injection tool for robustness assessment of java COTs

In line with market demands and the need for technological innovations, designing and implementing software and hardware components for computing systems is growing in complexity. In order to cope with such complexity whilst meeting market needs, engineers often rely on design integration with commercial-of-the-shelf-components (COTs). In the case where lives and fortunes are at stake, there is a need to ensure dependability of COTs in terms of their robustness before they can be adopted in such an environment. However, it is not often possible to thoroughly test COTs for robustness because their design as well as source codes are usually unavailable. In order to address some of the above issues, we have developed an automated software fault injection tool, ca lled SFIT, based on the use of computational reflection and Java technology. This paper describes our experiences with SFIT performing robustness testing of Java COTs, called Jada, a Linda tuple space implementation.

An Orchestrated Survey on T-Way Test Case Generation Strategies Based on Optimization Algorithms

The test case construction is amongst the most labor-intensive tasks and has significant influence on the effectiveness and efficiency in software testing. Due to the market needed for diverse types of tests, recently, several number of t-way testing strategies (where t indicates the interaction strengths) have been developed adopting different approaches Algebraic, Pure computational, and Optimization Algorithms (OpA). This paper presents an orchestrated survey of the existing OpA t-way strategies as Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Particle Swarm Optimization based strategy (PSTG), and Harmony Search Strategy (HSS). The results demonstrate the strength and the limitations of each strategy, thereby highlighting possible research for future work in this area.

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.