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Hybrid Ant Colony And Genetic Based Optimistic Software Testing

Sri Sai College of Engineering And Technology

Ankita, Sukhbeer Singh

[email protected] , [email protected]

Abstract: The review has shown that finding the best automation software testing is still an open area of research. Software testing involves much time for its testing, thus is expensive task. The comprehensive review has shown that the role of meta-heuristic techniques during software testing is ignored in most of existing research. The ant colony optimization technique suffers from poor convergence speed. The hybridization of ant colony optimization with genetic algorithm is also ignored. Therefore using the hybrid ant colony technique with genetic algorithm will be proposed in this work. The use of the proposed technique has ability to predict weather software can be used in real time or not.

Keywords: Software Testing, Software Testing techniques, genetic Algorithm

1. Introduction

Testing software in a mechanized way has been an objective for analysts and industry amid the most recent couple of decades. Still, achievement has not been promptly accessible. A few devices that are halfway computerized have advanced, while in the meantime the systems for testing programming has enhanced, in this way prompting a general change. Nonetheless, much programming testing is still performed physically and in this way inclined to mistakes, wasteful and immoderate.

In the previous couple of decades the developing impacts of programming in every aspect of industry have prompted a steadily expanding interest for intricate and dependable programming. As indicated by a study directed by the National Institute of Standard and Technology, roughly 80% of the improvement expense is spent on recognizing and redressing imperfections .The same study found that product bugs cost the United States economy around $59.5 billion a year, with 33% of this quality being credited to the poor programming testing foundation. Be that as it may, the mechanization of test information era is still a subject under examination. As of late, various techniques, for example, metaheuristic look, irregular test era and static investigation have been utilized to totally computerize the testing procedure, however the use of these instruments to genuine programming is still restricted.

2 Testing Levels Based on Software Activity

Tests can be gotten from prerequisites and details, plan antiques, or the source code. An alternate level of testing goes with each unmistakable programming improvement movement:

1. Acknowledgment Testing – Assess programming regarding necessities.

2. Framework Testing – Assess programming regarding structural outline.

3. Mix Testing – Assess programming as for subsystem plan.

4. Module Testing – Assess programming concerning definite configuration.

5. Unit Testing – Assess programming regarding execution.

3. Software Testing Techniques

Software testing procedures is broadly utilized as a part of various applications utilizing different testing techniques.

For the most part, programming testing methods are arranged into two classes: static examination and element testing .

In static examination, a code analyst scrutinizes the framework source code, declaration by explanation, and apparently takes after the honest to goodness venture stream by empowering an information. This sort of testing is exceedingly dependent on the examiner's experience. Static examination uses the framework necessities and layout chronicles for visual review. Alternately, dynamic testing frameworks execute the task under test on test data and watch its yield. Usually, the term testing suggests essentially dynamic testing. The accompanying subsections give a brief foundation on these two testing classifications.

 Static Analysis

For a significant long time, the greater part of the product engineers expected that the tasks are formed only for machine execution and are not proposed to be scrutinized by human, and that the most ideal approach to test a framework is by executing it on a machine. Along these lines began to change in the

mid 1970s, because of the Weinberg's work on "The Psychology of Computer Programming". Weinberg gave an influenced conflict to why ventures should be scrutinized by people and showed this could be a practical botch acknowledgment process.

i)Code Inspections

Code inspection is a course of action of approach and bumble revelation systems for social event code examining. Most talks of code examinations focus on the frameworks, structures to be balanced, and whatnot. In the midst of the appraisal session, two activities are driven: code depiction and code examination.

ii) Code Walkthroughs

This framework is indistinct to that of the examination strategy. The qualification, regardless, is in that rather than simply examining the undertaking or using bumble checklists,one of the individuals doled out as an analyzer goes to the meeting with a little course of action of paper tests that address sets of data and expected yield for the attempted framework or module. In the midst of the meeting, each investigation is objectively executed, i.e. the test data are walked around the method of reasoning of the framework. The state of the framework, i.e. the estimations of the variables, is kept an eye on paper or a written work board .

iii) Desk Checking

Desk checking can be seen as a one-singular audit or walkthrough; a man examines an undertaking, checks it concerning a mix-up rundown, and/or walks test data through it. There are three key reasons that work zone checking, for a considerable number individuals, is reasonably pointless: absolutely undisciplined methodology, the standard that people are overall deficient in testing their tasks, and no resistance like in the coordinated effort.

iv) Code Reviews

Code review is a framework for surveying obscure activities to the extent their general quality, common sense, extensibility, accommodation, and clarity. The purpose behind the study is to give programming engineer assessment.

• Dynamic Testing

Dynamic testing procedures execute the framework under test on test data and watch its yield .Usually , the term testing insinuates component testing. There are two sorts of component testing: black-box and white-box White-box testing is concerned with the degree to which test cases practice or cover the lucid stream of the framework.

i) White Box Testing

White-box testing is more for the most part associated. It is in like manner called reason scope testing or helper testing, since it sees the structure of the framework .The objective of white box testing is to routine of the unmistakable basis structures and streams in the undertaking. There are a couple White Box (Structural) testing criteria :

• Statement testing: Every declaration in the item under test must be executed at any rate once in the midst of testing. A more expansive and more grounded strategy is branch testing.

• Branch testing: Branch scope is a more grounded model than announcement scope. It consolidates explanation scope since every declaration is executed if every branch in a task is polished once. Regardless, a couple bumbles must be recognized if the declarations and branches are executed in a particular solicitation, which prompts way testing.

• Condition scope: For this circumstance, one forms enough investigations such that each condition in a decision handles each and every possible result at any rate once. Disregarding the way that the condition scope establishment appears, at first look, to satisfy the decision scope premise, it doesn't for the most part do in that capacity.

• Path testing: Regardless, way testing is generally seen as unfeasible in light of the fact that an undertaking with circle verbalizations can have an endless number of ways. A way is said to be 'conceivable', when there exists a commitment for which the way is explored in the midst of framework execution, by and large the way is unfeasible. Way scope has the upside of requiring uncommonly comprehensive testing.

ii) Black Box Testing

Black-box testing, Discovery testing, a.k.a. viable or determination based testing, tests the functionalities of programming against its point of interest, paying little regard to its structure.

1. Identicalness dividing sections the information region of a venture into a predetermined number of proportionality classes such that one can sensibly acknowledge (in the meantime, clearly, not ensure totally) that a test of an operator estimation of each class is equivalent to a test of whatever other quality inside the looking at class.

2. Limit esteem examination is those circumstances direct on, above, and under the edges of data correspondence classes and yield similarity classes.

Experience shows that analyses that explore limit conditions have a higher result than trials that don't. One inadequacy of point of confinement worth examination and balance isolating is that they don't research mixes of data as decision/condition scope does in white-box testing..

3. A cause-impact charting came to handle this issue. It is a formal tongue into which a trademark lingo determination is translated, which in like manner points out fracture and ambiguities in the specific. The graph is truly a modernized basis circuit (a combinatorial method of reasoning framework), yet instead of using standard equipment documentation, a to some degree simpler documentation is used. Subsequently, using equivalent comparability as a piece of white box testing, cause-sway outlining beats limit regard examination and proportionality allotting.

4. Mistake speculating is, all things considered, a natural and exceptionally designated strategy, whose procedure is difficult to formalize. This methodology needs a capacity that can smell out bungles. The central believed is to determine an once-over of possible oversights or errorprone circumstances and a short time later form test cases considering the summary.

3. Genetic Algorithm

"A genetic Algorithm is an iterative strategy keeping up a populace of structures that are competitor answers for particular space challenges. Amid every fleeting augmentation (called an era), the structures in the present populace are appraised for their viability as space arrangements, and on the premise of these assessments, another populace of competitor arrangements is shaped utilizing particular hereditary administrators, for example, multiplication, hybrid, and change."

Genetic Algorithm can be characterized as: "They join survival of the fittest among string structures with an organized yet randomized data trade to shape an inquiry calculation with a portion of the inventive pizazz of human hunt. In each era, another arrangement of simulated animals (strings) is made utilizing odds and ends of the fittest of the old; a periodic new part is striven for good measure. While randomized, hereditary calculations are no basic arbitrary walk. They proficiently abuse verifiable data to guess on new inquiry focuses with expected enhanced execution."

4. Literature Survey Dhananjay Thiruvady et al.(2014)[1]Contemporary engine vehicles have expanding quantities of robotized capacities to increase the security and solace of an auto. The car business needs to consolidate expanding quantities of handling units in the structure of autos to run the product that gives these functionalities. The product parts frequently require access to sensors or mechanical gadgets which they are intended to work. The outcome is a system of equipment units which can suit a set number of programming projects, each of which must be alloted to an equipment unit. They find that in spite of the substantial number of limitations, ACO all alone is the best strategy giving great arrangements by likewise investigating infeasible districts Yong Chen et al.(2008)[2] Automatic way arranged test information era is an undecidable issue and hereditary calculation (GA) has been utilized to test information era since 1992. For MATLAB, a multi-populace hereditary calculation (MPGA) was executed, which chooses people with the expectation of complimentary relocation in light of their wellness values. Applying MPGA to creating way situated test information era is another and important endeavor. A. Bouchachia et al.(2007)[3] This paper goes for fusing safe administrators in hereditary calculations as a propelled technique for taking care of the issue of test information era. The new proposed half and half calculation is called resistant hereditary calculation (IGA). A full depiction of this calculation is exhibited before exploring its application with regards to programming test information era utilizing some benchmark programs. Additionally, the calculation is contrasted and other transformative calculations. C.C. Michael et al.(2001)[4] examines the utilization of hereditary calculations (GAs) for programmed programming test information era. They portray the usage of our GA-based framework and analyze the viability of this methodology on various projects, one of which is fundamentally bigger than those for which results have already been accounted for in the writing. D.J.Berndt et al.(2004)[5] Highly perplexing and interconnected frameworks may experience the ill effects of discontinuous or transient disappointments that are especially hard to analyze. This exploration concentrates on the utilization of hereditary calculations for naturally producing substantial volumes of programming experiments. Specifically, the paper investigates two key systems for enhancing the execution of hereditary calculation experiment reproducing for high volume testing. The primary technique tries to abstain from assessing test bodies

of evidence against the genuine target framework by utilizing prophets or models. The second technique includes enhancing the all the more immoderate parts of hereditary calculations, for example, wellness capacity estimations. Xiajiong Shen et al.(2009)[6] A sort of programming test information computerized era strategy taking into account hereditary calculation and tabu inquiry calculation is proposed. Having both neighborhood look abilities of tabu pursuit calculation and worldwide hunt capacity of hereditary calculation, this tabu hereditary calculation joins tabu inquiry calculation with hereditary calculation. The analysis results demonstrate that the tabu hereditary calculation with tabu hunt as transformation administrator is successful on producing test cases and its streamlining execution is better than the basic hereditary calculation. E.Diaz et al.(2004)[7] Software testing is a costly and troublesome procedure which require much time. Consequently, the presence of devices that permit to diminishing this exertion is critical. This apparatus consequently creates test cases so as to acquire branch scope in programming testing from a source code. They portray the modules of the apparatus and present the outcome they have acquired contrasted the required time with produce the experiments with manual instrumentation and the required time with a programmed procedure Ruilian Zhao et al.[2003][8] demonstrates that allotment testing methodologies are generally inadequate in distinguishing shortcomings identified with little moves in info area limit. They display a creative programming testing approach in light of information space examination of particulars and programs, and propose the rule and technique of limit experiment determination in practical area and operational area. To consequently decide the operational space of a project, the ADSOD framework is prototyped. The framework underpins not just the determination of information space of whole number and genuine information sorts, additionally non-numeric information sorts, for example, characters and specified sorts. It comprises of a few modules in finding illicit estimations of info variables as for particular expressions. 5. RESEARCH METHODOLOGY

The various steps required to automatically test the software before its release.

Figure 1: Fow chart of proposed methodology

6. RESULT AND DISCUSSION

6.1 Experimental Setup

Proposed approach has been designed and implemented in WEKA. The Hybrid approach is compared with various algorithms. The WEKA is a high-performance language in simple and easy-to-use environment to compute, visualize and programming to express the problems and solutions in mathematical form, developed by Mathworks. WEKA uses various languages C, C++, Java, FORTRAN and Python. It is supported on windows, Unix and Macintosh. The various algorithms have been applied on the given dataset. Parameters are used for making comparisons between existing algorithms and proposed algorithm. Hybrid approach provides the better results than all algorithms when compared with them. The proposed algorithm is compared with different algorithms, which are Decision Stump, AdaBoostM1, Stacking, ASC, IBK and I/P Mapped Classifer.

6.2 Performance Analysis

This major section depicts the values of various parameters such as true positive rate, false positive error, precision, recall, f-measure, ROC area.

TP (True Positive): It denotes records that are predicted as true and they were actually true.

FN (False Negative): It denotes records that are predicted as false and actually they were true.

FP (False Positive): It denotes the records that are predicted as true actually they were false.

TN (True Negative): It denotes the records that are predicted as false.

Table 1: TP & FP Rate Comparison Table

This comparison table proves that the proposed work results are much better than the existing algorithms which is as shown in fig 2.

Figure 2: TP & FP Rate Graph

Precision: Precision is the part of retrieved instances that are appropriate. It is measure of exactness.

Recall: Recall is the fraction of related instances that are retrieve it is measure of completeness.

Table 2: Precision & Recall Comparison Table

This comparison table proves that the proposed work results are much better than the existing algorithms which is as shown in fig 3.

Figure 3: Precision & Recall Graph

F Measure: it combines and balance recall and precision. It is the compromise between recall and precision. When both are high

F measure is high. High value of F measure indicates better is the algorithm.

Algorithm

TP Rate

FP Rate

Decision Stump

0.855

0.855

ADABOOSTMI

0.953

0.201

IBK

0.829

0.643

ASC

0.883

0.455

STACKING

0.855

0.855

I/PMAPPED CLASSIFIER

0.855

0.855

Existing

0.867

0.595

Proposed

0.956

0.221

Algorithm

TP Rate

FP Rate

Decision Stump

0.731

0.855

ADABOOSTMI

0.951

0.953

IBK

0.807

0.829

ASC

0.873

0.883

STACKING

0.731

0.855

I/PMAPPED CLASSIFIER

0.731

0.855

Existing

0.846

0.867

Proposed

0.955

0.956

Table 2: F-Measure & ROC Comparison Table

This comparison table proves that the proposed work results are much better than the existing algorithms which is as shown in fig 4.

Figure 4: F-Measure & ROC Graph

Conclusion:

In this paper, we have evaluated the performance of ant colony optimization based software testing. Then propose a hybrid ant colony optimization with genetic algorithm to effectively test the software before its release. Further in this paper, we have compared the existing and proposed technique using following metrics such as true positive rate, false positive error, precision, recall, f-measure, ROC area. The proposed work results are much better than the existing results.

References

[1] Thiruvady, Dhananjay, et al."Constraint programming and ant colony system for the component deployment problem.", 14th international conference on computational science, vol. 29, pp. 1937-1947, 2014

[2] Y. Chen and Y. Zhong, "Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic Algorithm," 2008 Fourth International Conference on Natural Computation, Jinan, 2008, pp. 566-570.

[3] A. Bouchachia, "An Immune Genetic Algorithm for Software Test Data Generation," 7th International Conference on Hybrid Intelligent Systems (HIS 2007), Kaiserlautern, 2007, pp. 84-89.

[4] Michael, Christoph C., Gary McGraw, and

Michael A. Schatz. "Generating software test data by evolution." IEEE transactions on software engineering 27.12 (2001): 1085-1110.

[5] Berndt, Donald J., and Alison Watkins. "Investigating the performance of genetic algorithm-based software test case generation." High Assurance Systems Engineering, 2004. Proceedings. Eighth IEEE International Symposium on. IEEE, 2004.

[6] Shen, Xiajiong, et al. "Automatic generation of test case based on GATS algorithm." Granular Computing, 2009, GRC'09. IEEE International Conference on. IEEE, 2009.

[7] Díaz, Eugenia, Javier Tuya, and Raquel Blanco. "A modular tool for automated coverage in software testing." Software Technology and Engineering Practice, 2003. Eleventh Annual International Workshop on. IEEE, 2003. [8] Zhao, Ruilian, Michael R. Lyu, and Yinghua Min. "A new software testing approach based on domain analysis of specifications and programs."Software Reliability Engineering, 2003. ISSRE 2003. 14th International Symposium on. IEEE, 2003.

[9] Ng, S. P., et al. "A preliminary survey on software testing practices in Australia." Software Engineering Conference, 2004. Proceedings. 2004 Australian. IEEE, 2004.

[10] Abu, Ghaffari, Joao W. Cangussu, and Janos Turi. "A quantitative learning model for software test process." Proceedings of the 38th Annual Hawaii International Conference on System Sciences. IEEE, 2005.

[11] Bai, Xiaoqing, Chiou Peng Lam, and Huaizhong Li. "An approach to generate the thin-threads from the UML diagrams." Computer Software and Applications Conference, 2004. COMPSAC 2004. Proceedings of the 28th Annual International. IEEE, 2004.

Algorithm

TP Rate

FP Rate

Decision Stump

0.788

0.498

ADABOOSTMI

0.951

0.878

IBK

0.816

0.589

ASC

0.877

0.728

STACKING

0.789

0.5

I/PMAPPED CLASSIFIER

0.788

0.498

Existing

0.849

0.857

Proposed

0.953

0.914

[12] Liu, Zhenyu, Ning Gu, and Genxing Yang. "An automate test case generation approach: using match technique." The Fifth International Conference on Computer and Information Technology (CIT'05). IEEE, 2005.

[13] Chang-Ai, Sun, et al. "Architecture framework for software test tool." Technology of Object-Oriented Languages and Systems, 2000. TOOLS-Asia 2000. Proceedings. 36th International Conference on. IEEE, 2000.

[14] Last, Mark, and Menahem Friedman. "Automated detection of injected faults in a differential equation solver." High Assurance Systems Engineering, 2004. Proceedings. Eighth IEEE International Symposium on. IEEE, 2004.

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