A Novel Approach for Removal of Redundant Test Cases using Hash Set Algorithm along with Data Mining Techniques

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1 A Novel Approach for Removal of Redundant Test Cases using Hash Set Algorithm along with Data Mining Techniques Pandi Jothi Selvakumar Department of Computer Applications, AVC College (Autonomous), Mayiladuthurai, India Dr.K.Thyagarajan, Department of Computer Applications, AVC College (Autonomous), Mayiladuthurai, India Abstract: Software Testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain filtering techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. Authors used the Data mining approach, mainly because of its ability to extract patterns of test cases from complex and invisible data set. In Data Mining, there are numerous algorithms and methods available to extract irredundant data. In this paper, Authors generate test cases based on the Software Requirements Specifications document and apply Feature Selection: Filter method along with Hashing technique on those redundant test cases to make it irredundant. Later, the authors produce the comparison of the applied techniques to identify the number of unique effective test cases out a total. Keywords: Software Testing, Data Mining, Test cases, redundant, Clustering, Classification, SRS I. INTRODUCTION A. Software Testing Software testing is an investigation conducted to provide stakeholders with information about the quality of the product or service under test. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include the process of executing a program or application with the intent of finding software bugs (errors or other defects), and to verify that the software product is fit for use. Software testing can be conducted as soon as executable software (even if partially complete) exists. The overall approach to software development often determines when and how testing is conducted. For example, in a phased process, most testing occurs after system requirements have been defined and then implemented in testable programs. In contrast, under an Agile approach, requirements, programming, and testing are often done concurrently. Software testing involves the execution of a software component or system component to evaluate one or more properties of interest. In general, these properties indicate the extent to which the component or system under test: Meets the requirements that guided its design and development. Responds correctly to all kinds of inputs. Performs its functions within an acceptable time. Is sufficiently usable. Can be installed and run in its intended environments. Achieves the general result its stakeholder s desire. B. Test Scenario The purpose of test scenario (scenario testing) is to test the end-to-end functionality of a software application and ensure the business processes and flows are functioning as needed. In scenario testing, the tester puts themselves in the user s shoes and determines real world scenarios (use-cases) that can be performed. Once these test scenarios are determined, test cases can be written for each scenario. Test scenarios are the high level concept of what to test. In Simple, Test Scenario is determining what to be tested. C. Test Case A test case is a set of steps to be executed by the tester in order to validate the scenario. Whereas test scenarios are derived from use-cases, test cases are derived and written from the test scenarios. A test scenario usually has multiple test cases associated with it, as test cases layout out the low-level details on how to test the scenario. D. Data Mining Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Data mining involves six common classes of task they are, 1. Anomaly detection, 2. Association rule learning, 3 clustering, 4. Classification, 5. Regression, 6. Summarization. From the above task authors choose to apply filter method for various algorithms available to perform Clustering and Classification task. I. Classification RES Publication 2012 Page 13

2 Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. A classification task begins with a data set in which the class assignments are known already. Classifications are discrete and do not imply order. Continuous, floating-point values would indicate a numerical, rather than a categorical, target. A predictive model with a numerical target uses a regression algorithm, not a classification algorithm. Classification models are tested by comparing the predicted values to known target values in a set of test data. The historical data for a classification project is typically divided into two data sets: one for building the model; the other for testing the model. II. Clustering Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding to data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. While doing cluster analysis, we first partition the set of data into groups based on data similarity and then assign the labels to the groups. This clustering analysis allows an object not to be part of a cluster, or strictly belong to it, calling this type of grouping hard partitioning. In the other hand, soft partitioning states that every object belongs to a cluster in a determined degree. More specific divisions can be possible to create like objects belonging to multiple clusters, to force an object to participate in only one cluster or even construct hierarchical trees on group relationships. analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. II. PROBLEM DESCRIPTION When a test case is generated by the group of or individual tester using test scenario derived from the SRS document, there are enormous possibilities for redundant test case. Thus the redundancies in test case will increases the time and cost of testing process. The problem lies ahead of us is to remove the redundancy from the test case generated In case user requirements are not static, when the SRS is not predefined properly or a change is introduced to the development, hence the developer make changes in the functionality of the product it increase the complexity in understanding the test scenario for the tester it may also cause redundancy. Hence it s quite inevitable for the test engineer to find redundancy using existing market techniques. For this, authors use Feature Selection: Filter method of machine learning and data mining techniques to overcome the problem. A. Proposed Approach for Problem Solving E. HASH SET A hash table uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. Ideally, the hash function will assign each key to a unique bucket, but most hash table designs employ an imperfect hash function, which might cause hash collisions where the hash function generates the same index for more than one key. Such collisions must be accommodated in some way. In a well-dimensioned hash table, the average cost for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key-value pairs, at constant average cost per operation. In many situations, hash tables turn out to be more efficient than search trees or any other table lookup structure. For this reason, they are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches, and sets. F. WEKA Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning software written in Java, developed at the Waikato University, New Zealand. It is free software licensed under the GNU General Public License. Weka contains a collection of visualization tools and machine learning algorithms for data Figure 1. Flow of Control RES Publication 2012 Page 14

3 To reduce the redundant test cases, the authors, use the following approach. III. PROCESS OF IMPLEMENTATION A. Generating Test Case From the initial SRS document, the development team develops the module and converts it into.application. Simultaneously, the testing team prepares the test scenario then derives test cases for that module. Once the completed application package enters the testing phase the testers then dub the test cases with sample data in order to generate the test data set. B. Converting the Dataset into Attribute Related File Format to implement Data Mining using WEKA Before giving our test cases into our Machine Learning tool (WEKA), we have to convert it into the file format which is supported by the tool. Our tool supports only ARFF (Attribute Related File Format) or CSV (Comma Separated Values) files. This can be achieved manually or using the web resource available from Waikato University. C. Test case processing with WEKA Later, Load the generated data set file into WEKA then generate metadata like relation, attribute and generate graphical output over attribute. Once completed choose Classification menu and apply Naive Bayes Algorithm, later summarize of result, for the input with redundant test cases. Next apply Simple K-Means of Clustering menu, later summarize the result for comparison study. D. Implementation of Filter approach Once again load the generated data set with redundancy into WEKA then generate metadata like earlier once its completed load the filter file developed using hash set algorithm into appropriate provision. Again do the test cases processing with WEKA procedure then summarize the result of both the classification and clustering algorithm. Later, compare the result of both the process done with and without implementing hash set algorithm or filter, in order to find how effective, the method of hashing in removing redundancy. Thus the process once again proves the necessity to remove the redundancy. IV. RESULTS AND DISCUSSIONS In this section, the author gives the sample output by taking a simple program as an input file (labor. arff) with 17 instances and 77 records. Figure 2. Loading the Redundant Dataset without Hash Filter Figure 3. Loading the Redundant Dataset with Hash Filter No. of Attribute: 17 No of Instances: 77 Sum of Weight: 57 For Attribute: Duration Mean: Std Dev: No. of Attribute: 17 No of Instances: 57 Sum of Weight: 57 For Attribute: Duration Mean: Std Dev: RES Publication 2012 Page 15

4 Figure 4. Applying Naïve Bayes Algorithm without Hash Filter Figure 5 Applying Naïve Bayes Algorithm without Hash Filter Correctly Classified Instances % Incorrectly Classified Instances % Kappa Statistic Mean Absolute Error Root Mean Squared Error Correctly Classified Instances % Incorrectly Classified Instances % Kappa Statistic Mean Absolute Error Root Mean Squared Error Figure 6. Applying Simple K-Means Algorithm without Hash Filter Figure 7 Applying Simple K-Means Algorithm with Hash Filter Clustered Instances 0 23 (30%) 1 54 (70%) Clustered Instances 0 48 (84%) 1 9 (16%) Figure 8. Comparison of resultant dataset from WEKA using simple file comparison tool RES Publication 2012 Page 16

5 V. CONCLUSION AND FUTURE WORK The paper focused on the applicability of data mining feature selection: filter techniques in reducing the number of test cases by removing those which are redundant. For future we try to break the limitation of applying the filter to group of attribute into single attribute thus it increases the efficient for reducing redundancy to a greater extent. REFERENCES [1]. Test case reduction techniques Survey, MarwahAlian, Dima Sulieman, Adnan Shaout, International Journal of Advanced Computer Science and Applications, (IJACSA), Vol. 7, No. 5, [2]. A Survey on Automatic Bug Triage Using Data Mining Concepts, K. ReshmaRevathi, Dr. S. Kirubakaran, International Journal of Science and Research (IJSR) ISSN (Online): Index Copernicus Value (2015). [3]. Critical Review on Test Case Generation Systems and Techniques, Shivani Kaushik, Ajay Kumar,International Journal of Computer Applications ( ) Volume 133 No.7, January [4]. Asurvey on automatic test case generation, M.Prasanna, S.NSivanandam, R.Venkatesan and R.Sundarrajan, Academic Open Internet Journal Vol.15, [5]. Generating test cases from use cases - Rational Software, Heumann,J., naledge/jun01/generatingtestcasefromusecasesjune01.pdf [6]. Amalgamation of Automated Test Case Generation Techniques with Data Mining Techniques: A Survey, Yogita Dubey, Divakar Singh, Anju Singh, International Journal of Computer Applications ( ) Volume 134 No.5, January [7]. Reliable Mining of Automatically Generated Test Cases From Software Requirement Specification (SRS), Lilly Raamesh and G.V. Uma, IJCSI Vol. 7, Issue 1, No. 3, January [8]. Automatic software test case generation, MR Keyvanpour, H Homayouni, and HaseinShirazee, Journal of Software Engineering, 5(3):91 101, [9]. A Review: Study of Test Case Generation Techniques, IttiHooda, RajendarChhillar, International Journal of Computer Applications, 2016 by IJCA Journal, Volume Number 16. [10]. Improving the Effectiveness of Software Testing through Test case Reduction, Mohapatra, R. P, Singh, Jitendra, Word Academy of Science, Engineering and Technology 13, page , [11]. Test Case Generation and Test Data Extraction Technique, Pakinam.N. Boghdady, Nagwa L. Badr, Mohamed Hashem, TolbaF. Mohamed. [12]. Using Genetic Algorithm for Automated Efficient Software Test Case Generation for Path Testing, Nirpal. PB and Kale. KV, International Journal on Advanced Networking and Application, Vol: 02, Issue:06, Pages: , April [13]. Basic Principle for Test Case Generation Automatically, Mishra. Manish, Mishra. Shalini, Porawal, Robins, VSRD International Journal of Computer Science and Information Technology, VSRD-IJCSIT, Vol. 2(9), pg , [14]. An Orchestrated Survey on Automated Test case Generation, Journal on Systems and Software X(y), xxcyy. SaswatAnand et al., 20xx, Antonio. Bertolino, J.Jenny.Li, Hong.Zhu(Editors/Orchestrators), Proceedings of IEEE/ACM Workshop on Automation of Software Test(AST 06-AST 12): Feb 11,2016. [15]. Generation of Test Cases from Functional Requirements. A Survey, Guitieuez. Javier J, Escalona.Maria. J,Mejias Manuel, Toues Jesus. [16]. Journal of Software: Evolution and Process, Software Process: Improvement and Practice, Software: Practice and Experience, Jeff Offutt and Robert M. Hierons, Impact Factor: 1.082, ISI Journal Citation: 2017: 46/106 (Computer Science Software Engineering). Online ISSN: [17]. Entry & Exit Criteria in Software Testing, January 20, 2017, ThinkSys development. [18]. Practical Steps to Improve Your Testing Skills, January 15, 2017, ThinkSys development. [19]. Ultimate Testing Checklist, November 21, 2016, ThinkSys development. RES Publication 2012 Page 17