Electronic Journal of Biology

Keywords

Ant colony optimization (ACO); Artificial bee colony (ABC); Genetic algorithm (GA).

1. Introduction

The process of testing a software system before its actual use is essential for delivering error free software. The system is checked by comparing the expected output with the actual output after executing sample input(s) [1]. If both expected and actual outputs are same then there is no error else the system contains an error. Thus the aim of software testing is to find faults within the system by executing it with sample input data. There are number of software testing techniques such test case optimization and prioritization [2-9] which are proposed in preceding years to reduce various resources such as time, human Intervention in testing phase.

A major area of research is in Structural Testing which takes into account the code, structure of code and internal design. Commonly used techniques for structural testing [8-12] are that include control flow/ coverage testing, basic path testing, loop testing, and data flow testing [13]. Recent studies in testing indicate that a major work is in area of test case generation and prioritization based on experimental proofing. The techniques that are used in such studies are inspired from Artificial intelligence [2-5,14-16]. Therefore a part from the above mentioned techniques a new focus for the researchers is how to map natural intelligence to Artificial intelligence for solving NP hard problems, one such nature inspired intelligence called metahurestic search gives us optimized solutions for a problem in hand. In this paper we discuss various metahurestic techniques on which research work is going on very rapidly.

1.1 The ant colony: Metahurestic

Ant algorithms are one of the most attracting areas of research in optimization of software testing. The natural foraging behaviour of real ant’s in wild space was first modelled by Dorigo, Maniezzo, and Colorni [8]. The indirect communication between ants within the colony using pheromone secretion provide a most powerful path for optimization methods. The ant colony algorithms are considered to be a part of Swarm intelligence i.e. multi- agent systems are based on the behavior of natural real world insects acting as swarm and collectively forming optimization behavior while foraging. A number of such swarm based algorithms has been proposed such as artificial bee colony, artificial bee colony optimization and particle swarm optimization. In this section a details discussion on the idea behind ACO is put forwarded to solve software testing problems in much easy way as compared to the traditional approach.

1.2 Ant in the real world-a wild metaphor

In 1959 Grasse [17] first introduced a term known as Stigmergy to the indirect form of communication among multi-agents evolving as a self-organized single system while modifying their local environment. In 1990 Deneubourg, Aron, Goss, and Pasteels [18] studied the ant colonies Stigmergy nature in which each ant communicate indirectly by depositing a chemical known as pheromone on their path and the ant then tends to follow that path. Goss, Aron, Deneubourg, and Pasteels [18] with their experiment show that ants converge to the trail having higher concentration on pheromone deposit. Let us demonstrate a simple - Shortest bridge experiment ? as conducted by (Goss et al.) [17]. Figure 1 shows the diagrammatic view of setup in which two possible paths from source (nest) to destination (food) are shown. Path A is shorter than path B. Initially as the ant move from the nest foraging for food, it follow a random path but as the time passes we observed that all ants following the path A. This is due to the fact that when initially an ant move from her nest following path A and after collecting food she will reach to nest again in less time than an ant which had followed the path B due to the distance parameter and in this way it had deposited a pheromone before the second ant following the path B. Thus a convergence to a shortest path is achieved (Figure 1). The experiment shows the exploration to exploitation due to pheromone deposition tendency of ants in real world.

Figure 1: Two possible path for foraging.

1.3 From nature to artificial computers

In the following section, we briefly explain how the ants’ behavior, as well as pheromone evaporation can implement.

1.4 Probabilistic forward ants and solution construction

In ACO, there are two mode of working, the first is called forward move and the other is called backward move. In forward mode the ant’s movement is from nest (i.e. the source) to the food (destination) and Vice-versa in backward mode. After reaching to food source the ant reverse its direction .i.e. a shift to backward direction in order to reach to the source. The solution in forward moves is built by taking a probabilistic decision for next node to move to among those in the neighborhood of graph node on which they are located. (Given a graph G = (N,A), two nodes i, j~N if there exists an arc i, j~A). This probabilistic choice is particularly biased on pheromone deposited and Heuristic value between two nodes.

1.5 Deterministic backward ants and pheromone update

The use of an explicit memory allows an ant to repeat the path it had followed while building solutions. While moving backward, PP-ACO ants leave pheromone trail on the arcs they traverse.

1.6 Pheromone updates based on solution quality

In ACO the ants memorize all nodes that are traced through forward move, as well as the associated arc’s cost if the graph is weighted. They can therefore evaluate the cost of the solutions they build and use this evaluation to modulate the amount of pheromone and heuristic they deposit while in backward mode. Making pheromone and heuristic update as function of the generated solution quality ensure in directing future ants more strongly toward better solutions.

1.7 Tour building

In Ant systems, the tour in a control flow graph (path) is concurrently builds by m number of ants. An ant start from an initial node and by using random proportional rule the ant decides the next node to be visited upon. The probability with which ant k, at node i, chooses the node j is given as

Where N_ij=1/d_ij is called heuristic which is priori available and it indicates the visibility of a path so that an ant can follow a path having higher heuristic. N_i^k is the feasible neighbourhood of ant k at node i i.e. the set of node in CFG which an ant has not visited (P_ij^K for choosing a node outside N_i^k is 0). The effect of pheromone and heuristic on next move is determined by the value of α and β. Setting α=0, will select the most closet node, if β=0 then only deposited pheromone trail influence the next move and it may leads to poor solutions. In situations when a>1, a stagnation situation occurs in which all the ants follow the same path and construct the same tour, which, results in sub-optimal solutions [8]. Thus it is extremely important to choose these parameters very carefully. Good parameter values for the various ant Algorithms are given in Table 1 [19]. In the following section, we briefly explain how the ants’ behaviour, as well as pheromone evaporation can implement.

Table 1: Parameter setting for various ACO algorithms.

1.8 Pheromone updation: A sequential way

After an ant has constructed its tour, the updation in pheromone is completed by lowering the pheromone value on all arcs visited by the ant by a constant amount (or factor). This is also called as pheromone evaporation. The left pheromone value is then added to the nodes an ant has visited in their tour. Pheromone evaporation is given as

t_ij←(1-ρ) t_ij

Where 0<ρ<1, and ρ is the pheromone evaporation rate. ρ controls the indefinite addition of the pheromone thus permitting the algorithm to overlook previously taken bad decisions. The pheromone deposition after evaporation is defined as

t_ij←t_ij +Δ t^k_ij

Where Δ t^k_ij is the pheromone deposited by kth ant on the visited node, which is defined as:

Where C_l^k is the tour’s length built by the k^th ant, which is given by the summation of arc’s length which belongs to T^k.

The heuristic updation is given as Δ nij←no/C_i^k

In the Table 2 [20-35] we briefly discuss the various works that has been contributed by various researchers in field of software testing using ACO

Table 2: Survey on Ant colony approach in automated software testing.

1.9 Artificial bee colony optimization

Reaction diffusion model of a honeybee colony’s foraging behavior [36]. Tereshko was the first who mapped the foraging behavior of honeybee based on reaction diffusion equations which results in collective exploration and exploitation of food source (i.e. candidate solutions) by swarm (honeybees) [37]. The model has three basic components as

• Food Sources: In order to select a food source, a forager bee evaluates several properties related with the food source such as its closeness to the hive, richness of the food, taste of its nectar, and the ease or difficulty of extracting this food. For the simplicity, the quality of a food source can be represented by only one quantity although it depends on various parameters.

• Employed foragers: An employed forager is the bee who is currently exploiting the food source i.e. hunting the food source. After collecting the nectar (food) from the food source she gives the information about the various parameters such as food quality, distance of food source from hive, direction of food source to the bees in the hives (onlooker).

• Unemployed foragers: These are the bees that are continuously looking for the food to exploit it. They are either the scout bees that are searching the space near to hive randomly or the onlooker bees that are waiting for the employed bee to transfer the information about the food source and once they get information they went out of the hive to go in the direction of food. After leaving the hive they themselves act as an employed bee once they get the food source. On an average there are about 5-10 % of scout bees.

The exchange of information about the food is totally depends on the dance of employed bees in the hive, once the employed bee get the food and return to the hive , she dances on the floor called as waggle dance. The onlookers closely watch this waggle dance on the dancing floor and get employ herself at the most profitable source depending on the dance. In this way a recruitment process has been started i.e. onlookers are recruited to become employed bees.

Let us try to understand the whole process using Figure 2. We assume that there are two food sources A and B. The source A is more rich i.e. higher nectar than source B. Initially assume that two bees act as forger (searching for food) about 5-10% are scouts and the rest are onlooker bees in the hive. So these initial two bees act as a scout bees and let bee (B1) goes to food source A and the other bee (B2) goes to food source B. Once they find the food source and collect the nectar from it they become employed bee E1 and E2 for B1 and B2 respectively. After collecting the food she returns to the hive and start dancing on the dancing floor. As two bee collect different information about the food source so there dance is different. Onlooker’s bees carefully watch the dance of these two bees and decide which food source they choose and in this way onlooker bees become employed bees and reached to the food source.

Figure 2: Honey bee wangle dance sharing information.

An Artificial Bee Colony Algorithm: A simple ABC algorithm is given in Listing 1.

ABC has been used in number of ways in different automated software testing approaches. A detail list of the survey is illustrated in the Table 3 [38-64].

Table 3: Survey on bee colony approach in automated software testing.

From the literature survey it has been clear that ABC has potential to solve many problems in automated software testing domain. a critical insight from the survey suggest that ABC has been used to make software testing more reliable, time efficient and better resource utilization. It is thus again an open research area in automated software testing.

1.10 Genetic algorithm

Genetic Algorithm was proposed by Holland [2]. GA is heuristic search algorithms that is inspired by natural biological evolution of species and solve a variety of optimization problems to improve the quality of the search. A simple GA algorithm is shown in Listing 2.

The general flow chart for the same is shown in Figure 3. A research survey on the use of genetic algorithm in automated software testing has been conducted as shown in Table 4 [65-85].

Figure 3: Flowchart of a simple genetic algorithm.

Table 4: Survey on genetic algorithm approach in automated software testing.

A Literature survey study among the various metahurestic techniques used in automated software testing has been discuss above which clearly indicate frequently used metahurestic for automated software testing during the last few years. The survey also indicates the potential of using metahurestic as a search technique in software testing [86-89].

2. Conclusion

In this paper we analyze scope of research that can be carried out in computer science especially in field of software testing using nature inspired algorithms such as ACO, ABC AND GA. From the survey we conclude that during the last few decades there has been a significant change in testing approaches. Metahurestic approaches are slowly replacing the Manual and traditional testing activities. We have a strong observation that these nature inspired algorithms are not only useful on computer science but also in other related filed such as electronics (making optimized control flow, load balancing, circuit optimization and related optimization problems) and biology such as protein folding prediction. Thus metahurestic is growing area for research and is open to carry out new researches. We have observed that researchers are moving from traditional algorithms to natural inspired algorithms in solving NP hard problems.

Acknowledgements

This Work was supported by university grant commission (UGC), Government of India for Doctoral Research Study under Grant No. F./201415/ NFO201415OBCDEL16123. The authors are very grateful to the anonymous reviewers for providing valuable and detailed comments that have greatly improved the paper.

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