A review of applications of search based software engineering ...

A Review of Applications of Search Based Software Engineering Techniques in Last Decade Parul Gupta1, Ishani Arora2, Anju Saha3 1

Department of Computer Science and Engineering, Northern India Engineering College FC-26, Shastri Park, New Delhi – 11053, India [email protected] 2,3 University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University Sector-16C, Dwarka, Delhi – 110078, India 2 [email protected] 3 [email protected]

Abstract: Optimization and testing in software engineering using traditional techniques has become a tedious task now-a-days. In order to fasten this process, search based-software engineering (SBSE) techniques are introduced to solve real world large scale problems efficiently. Also, previous researches have shown a significant contribution of SBSE techniques in the domain of software testing, reliability, cost and effort estimation. This work reviews the application of five major SBSE methods and the issues solved by different practitioners in the last decade. It also addresses the problems in the implementation of these techniques which are still open for further research Keywords: Metaheuristics, Search Based Software Engineering, Software Engineering, Software Quality, Software Testing.

I.

INTRODUCTION

The term Metaheuristic is composed of two words, one is ‘heuristic’ which means ‘to find’ and it is prefixed by another word ‘meta’ i.e. ‘beyond, in an upper level’. S. Voß et al. [1] defined metaheuristic as, an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions. It may manipulate a complete (or incomplete) single solution or a collection of solutions at each iteration .The subordinate heuristics may be high (or low) level procedures, or a simple local search, or just a construction method. Metaheurisitic techniques have been classified in literature through various perspectives [2]. The amount of available solution space used by an algorithm at specific time period forms the basis for first classification. Hence it can be either a single point based search or a population based search. Variable neighborhood search (VNS), iterated local search and tabu search falls in the category of single point search metaheuristics. It discovers a path or trajectory in its own space of search and hence also called as trajectory methods. Population based search involves evolution of group of points in search space such as in ant colony optimization and evolutionary computation techniques. The mode of origin is another basis to distinguish between nature inspired and no 978-1-5090-1489-7/16/$31.00 ©2016 IEEE

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nature inspired metaheuristic algorithm. Evolutionary computation and Ant Colony Optimization belongs to the class of nature inspired whereas tabu search and iterated local search belongs to the class of non nature inspired algorithms. Metaheuristic algorithms are also known as search based techniques or optimization algorithms which when applied to software engineering problems gave rise to Search Based Software Engineering (SBSE) [3] which reformulates software engineering problems as ‘search problems’. It is a collective attempt to shift from human based search to machine based search by applying search based optimization techniques. These methods encompass– representation of problem and defining fitness function. SBSE treats search problem as one where optimal or near optimal solution are sought in a search space of solution by making use of fitness function that discriminates worse and better solution. Although many researchers, practitioners have applied search based optimization to software engineering domain, SBSE term was first coined by Harman and Jones [3] in 2001 in their work which advocates Search Based Optimization as a general approach to Software Engineering. A number of SBSE methods such as simulated annealing (SA), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO),tabu search (TS), hill climbing etc have been applied in different activities of software development such as project management [30,32], automated test case generation [10,18], software cost estimation [38,40,44] etc. This study provides a review of the five major metaheuristic techniques, namely, SA, GA, ACO, PSO and TS applied by the researchers in the last decade i.e. from 2005 to 2015 to solve different problems in the domain of software engineering. The paper is organized as follows: Section II presents a brief study of a total of 35 papers in the field of search based software engineering from the last 10 years, open & still unresolved issues in this area are discussed in section III and conclusions and future directions are presented in section IV.

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II.

SYSTEMATIC LITERATURE REVIEW (SLR) PROCESS

RQ-2: Implementation issues The metaheuristic techniques have been applied to direct the various problems in software development life cycle such as estimation and optimization of software’s cost and effort, automatic generation of test data and so on. Various authors [10, 13, 18, 53] applied the different SBSE techniques for automatic test data and test case sequences generation. No key and true potential of any of these techniques has been recognized fully till date to generate the test data automatically for all kind of software’s testing in an efficient manner. Hence a need for a generalized technique arises.

The papers in this study are reviewed in concordance with the metaheuristic technique employed by different researchers to address software engineering problems. These 35 papers include 6 conference proceedings, 25 journal articles and 4 others such as symposium and workshop articles. A. Aim of Research This study is carried out with the aim to answer the following research questions:

Another issue which came in front is that newly proposed techniques such as PSO and TS are not independently working but most of them are hybridized along with conventional techniques like GA [39, 45, 51] in different ways to address the prevailing issues in software engineering. So, these novel methods are still required to find their unique and independent way of implementation in domain of software engineering.

RQ-1: What are the major application areas in which SBSE techniques were employed to address the several issues involved in software engineering domain? RQ-2: What are current trends and issues related to their implementation in software engineering? RQ-3: Identification of researchers who have contributed significantly in SBSE. B. Sources and Process

Thirdly to account for efficiency of the implemented and proposed algorithm, different practitioners used different performance comparison factors. C. Mao et al. [34] have used average coverage, success rate, average generation and average time as the performance accounting metrics for their proposed approach comparison with existing techniques but E. Díaz et al. [47] have used branch coverage percentage as comparison measure and again T. S. Rao et al. [40] have used MARE and prediction metrics. Therefore, there should be standardized measures along which the techniques should be evaluated and compared so that a uniform result can be achieved and followed.

Below mentioned are the core online sources used for extracting information: a. b. c. d. e.

IEEE Xplore (http://ieeexplore.org/) ACM digital library (http://dl.acm.org/) Google Scholar (http://scholar.google.co.in/) Springer LNCS (http://www.springer.com/gp/) Science Direct (http://www.sciencedirect.com/)

C. Criteria’s Involved in Study Selection The selection of techniques is performed keeping in view the way they are able to solve most of the issues lying in the software engineering domain. This review study covers the following five major SBSE techniques.

C. RQ-3: Significant Researchers The most consistent contribution in identifying the applications of metaheuristic techniques in domain of software engineering has been done by E. Alba, F. Chicana, A. S. Ghiduk, Dr. A. Kaur, P. R. Srivastava and T. S. Rao.

a. Simulated annealing b. Genetic algorithm c. Ant colony optimization d. Particle swarm optimization e. Tabu search

V.

CONCLUSION AND FUTURE SCOPE

This paper reviewed the literature describing the applications of SBSE in the last decade i.e. from 2005 to 2015. Different problems were recognized and addressed by the several software engineering practitioners and researchers. It also explains the data sets applied in their experimental work, the evaluation measures and the methodology followed during the implementation to solve a particular problem.

III. STATE OF THE ART Table 1 summarizes the application of five major metaheuristic techniques, applied by researchers for solving different problems in the domain of software engineering. IV. RESULTS

The work provides a brief view of what has already been done till date, but is not a comprehensive view of all the work done till now. It will guide the practitioners to explore the problems which are still open in SBSE area and hence, resolve them by providing an effective, efficient and a viable solution for the same.

RQ-1: Major Application Areas SBSE techniques have been used in numerous fields to solve distinct problems as shown in Table 1. Some of the major ones identified are: automation of test case generation, cost estimation, effort estimation, prediction of software faulty modules, optimization of coefficients of COCOMO models. 585

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TABLE I: SUMMARY OF APPLICATION OF SBSE TECHNIQUES

Authors

Year

Problems Addressed

Other Techniques /Models Applied

Data Sets Used for Validation

Metrics/ Comparison techniques

Simulated Annealing (SA) Example from telemetry network system, AT&T Bell laboratories [5] Quadratic assignment, two calendar & boiler problem

Diameter, autocorrelation, generation rate of better solutions

SA and Bayesian classifier

Java API classes

J-index

Probabilistic NN exhaustive search

Data sets from PROMISE [9] repository

Recall

Test case for testing the target path

Target paths and proper fitness function

Six distinct programs

GADGET [12] technique

GA

Random numbers generated by code

Fitness function

GA

Min-max algorithms

Time order decline [16, 17]

P. F. Pai and W. C. 2006 Hong [4]

F Software Reliability

SVM-SA model

H. L. Waeselynck et al. [6]

Test Generation, safety critical property, verification, testing

SA, landscape concept

S. Bouktif et al. [7] 2006

Maximize OO model accuracy

P. C. Pendharkar [8]

Software defect prediction model learning problem

2006

2010

Genetic Algorithm (GA) J. C. Lin and P. L. 2000 Yeh [10] J. Miller et al. [11] 2006 P. R. Srivastava [13]

2009

F. S. Babamir et al. 2010 [15] A. Pachauri and Gursaran [18]

2010

M. A. Alshraide et 2013 al. [19]

Failure of earlier test case generators Automatic test data generation Optimized software testing Automation of structural-oriented test data Automating the test case in java programs unit testing Test data that fulfils the branch criteria for testing PL/SQL programs

GA based path testing algorithm GA and Test Data Generation

GA, Java modeling language (JML) GA GA, Control flow graph based methodology

K. K. Rao et al. [20]

2013

Optimizing software testing efficiency

A. Puri and H. Singh [21]

2014

Fault susceptible critical GA classes

Ant Colony Optimization (ACO) Combinatorial E. Alba and F. 2007 optimization problem Chicano [23] construction of graphs A. S. Ghiduk [25]

2010

Production of optimal paths set

Branch coverage Gaussian distribution, number creep & average coverage ratio Critical path clusters

jEdit project

8 different measures [22] & confusion matrix

ACO for huge graphs

missionary technique

Five Promela models [24]

ACO, PCTDACO prototype

C++ programs

Number of nodes

Y. Singh et al. [26] 2010

Rearrange the test cases ACO & regression within time limit test prioritization

B. Sharma et al. [27]

ACO based tool Test sequences in state efficient software transitions optimal paths coverage

2011

Java framework JGAP, Java’s software Soda-vending machine 6 real objects of Jordan university hospital information

Sum of square errors, Normalized root mean square error

586

Fault coverage capacity, Average percentage of fault detected State diagram of class Compared with management system conventional PPTACO state machine [28] [29] Eight test cases containing 10 faults

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Authors

Year

J. Xiao et al. [30]

2012

B. Crawford et al. [32]

2014

C. Mao et al. [34]

2015

S. Yang et al. [35] 2015

Problems Addressed

Other Techniques /Models Applied

ACO system software Instance generator project scheduling solve [31] NP-Hard SPSP problem ACO, max-min ant SPSP system [33], hypercube framework Generating test data for structural testing

Data Sets Used for Validation

SPSP model, 30 tasks Hit rate & average quality instances of efficient solution Different number of tasks and employees

ACO, local transfer Eight significant & global transfer programs

Enhanced existing ACO Comprehensive algorithm improved ACO

Metrics/ Comparison techniques

GA for comparison Average coverage, success rate, average generation time, ANOVA

Classic triangle Iterations number, search classification program ability, random generation

Particle Swarm Optimization(PSO) A. Windisch et al. [36]

2007

Software testing

PSO, 25 simple artificial & Test case coverage, t-test DaimlerChrysler test 13 complicated system [37] industrial test objects

T. S. Rao et al. [38] 2011

Software cost estimation PSO, Fuzzy logic

A. Kaur and D. Bhatt et al. [39]

Regression testing

2011

P. R. P.V.G.D et al. 2011 [40] B. S. Ahmed et al. 2014 [41] F. S. Gharehchopogh et 2014 al. [44] M. R. Girgis et al. [45]

2015

Tabu search(TS) M. Casertaa and A. 2009 M. Uribe [46] E. Díaz et al. [47]

2008

F. Ferrucci et al. [48]

2009

A. Sharma et al. [50] J. Kaur and R. Sindhu [52]

2010 2014

G. Pandiyan and P. 2015 Krishnakumari [53] T. B. Miranda et al. 2015

NASA software data

Hybridized particle swarm optimization Java‘s IDE (HPSO), GA Multi Objective Two data sets from Software cost estimation PSO(MOPSO) COCOMO projects GUI functional testing

Simplified swarm optimization

Fuzzy c-means, Software cost estimation hybridized PSO model Genetical swarm Automation of test paths optimization generation technique

‘Flight Reservation’ case study Software projects of NASA data set

Four benchmark problems Different C/C++ programs

Software development effort estimation

TS

Desharnais dataset [49]

Optimizing the testing cost Optimizing coefficients of COCOMO II Elimination of defects and automate the fault localization

Dijkstra’s algorithm, Program of ship TS charges Turkish and Industry TS, GA data set Arcene and lung TS fault localization cancer data sets (UCI repository)

587

Execution time MARE and prediction Pair-wise independent combinatorial [42], Test vector generator [43] Magnitude of relative error (MRE), Relative error deviation (PRED)

15 C# small programs PSO, GA for comparison

Memory-mechanism for TS problem solving Automatic structural TS generator software testing (TSGen)

Generation of improved TS, GA

Mean absolute relative error (MARE)

Sample voter

Tabu tenure Random generator PRED, Mean magnitude of relative error (MMRE), median MRE Halsted’s measures [51] GA Fault prediction rate, software repair rate, repair cost time GA

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Authors [54]

Year

Problems Addressed

Other Techniques /Models Applied

test-case sequences

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