Testing of software requires a great amount of time and effort. The tester's main aim is to d... more Testing of software requires a great amount of time and effort. The tester's main aim is to design optimized test sequences with a minimum amount of time, effort, and with less redundancy. Testers have used artificial intelligence meta-heuristic algorithms for optimization of test sequences. The model-driven approach is helpful in the generation of test sequences at early designing phase only. The model-driven approach uses UML diagram to represent the system's behavior and design test cases for the system at design stage of software development life cycle. The proposed approach uses natural river system for optimizing favourable non-redundant test path sequences using UML activity diagrams and sequence diagrams. The implementation of proposed approach has been done using python and results show that the proposed approach provides full coverage of test paths with less redundant test nodes compared to other meta heuristic algorithms.
Software complexity increases with the increase in functionality. To handle the complexity, unifi... more Software complexity increases with the increase in functionality. To handle the complexity, unified modeling language (UML) has been used to structure the designs. The model is further used as an input to generate optimized test paths. In this paper, a new test path optimization tool has been generated for the automatic generation and prioritization of test paths. In the first phase, an activity diagram has been created using “Visual Paradigm” software. It is then exported as an XMI file (XML metadata interchange). The aim is to generate adjacency matrix by parsing the XMI file. After creating it, the top-down approach is applied to generate the test cases. An algorithm called firefly algorithm is used to optimize and prioritize the critical paths. The priority to the test paths is assigned according to the values of information flow and cyclomatic complexities of each node. Using this algorithm, the redundancy of the tests paths was removed.
Testing of software requires a great amount of time and effort. The tester's main aim is to d... more Testing of software requires a great amount of time and effort. The tester's main aim is to design optimized test sequences with a minimum amount of time, effort, and with less redundancy. Testers have used artificial intelligence meta-heuristic algorithms for optimization of test sequences. The model-driven approach is helpful in the generation of test sequences at early designing phase only. The model-driven approach uses UML diagram to represent the system's behavior and design test cases for the system at design stage of software development life cycle. The proposed approach uses natural river system for optimizing favourable non-redundant test path sequences using UML activity diagrams and sequence diagrams. The implementation of proposed approach has been done using python and results show that the proposed approach provides full coverage of test paths with less redundant test nodes compared to other meta heuristic algorithms.
2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)
Storage provided by cloud is the valuable and significant utilization of cloud services for cloud... more Storage provided by cloud is the valuable and significant utilization of cloud services for cloud users. Cloud systems are useful for data sharing among the group and give a large number of benefits to users. Major characteristics of cloud computing includes integrity, confidentiality, privacy and accountability. It allows sharing of files between different users using cloud computing – based technologies. In this paper, we have categorized various cryptographic schemes for secure data sharing in cloud with their merits and demerits.
Data mining is essentially the discovery of valuable information and patterns from huge chunks of... more Data mining is essentially the discovery of valuable information and patterns from huge chunks of available data. Two indispensible techniques of data mining are clustering and classification, where the latter employs a set of pre-classified examples to develop a model that can classify the population of records at large, and the former divides the data into groups of similar objects. In this paper we have proposed a new method for data classification by integrating two data mining techniques, viz. clustering and classification. Then a comparative study has been carried out between the simple classification and new proposed integrated clustering-classification technique. Four popular data mining tools were used for both the techniques by using six different classifiers and one clusterer for all sets. It was found that across all the tools used, the integrated clustering-classification technique was better than the simple classification technique. This result was consistent for all t...
2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS)
Fundamentally, a sentiment refers to the reflection of emotions of people. Today's world stan... more Fundamentally, a sentiment refers to the reflection of emotions of people. Today's world stands on the strings of emotions. People express happiness, sadness, love, hatred etc. through some actions. Division of emotions i.e positive, neutral and negative, is called sentiment analysis. Nowadays there is a sentiment rich data in the form of tweets, status updates, blog posts, reviews, comments, forums for discussion etc. If we efficiently work upon this bucket full of sentiment rich data, it gives way in apprehending the opinions, views or perspective of masses in a specific functional area. Moreover, the result of this analysis will aid people in taking suitable actions or corrective measures for their growth. This effort of ours is like a drop in the ocean to try to analyze the reviews posted by people at four different websites (airlinequality.com, Amazon, Yelp, and IMDB). Further, the reviews are processed and analyzed using machine learning procedures, algorithms and other related aspects. Finally, the conclusion is derived by finding the polarity of a particular review whether it is poor, average or excellent for Airlines dataset and 0 or 1 for the other three datasets. The entire task was performed using Python.
Abstract Structural testing is used to test the internal structure or code of the program in whic... more Abstract Structural testing is used to test the internal structure or code of the program in which knowledge to the internal structure is must to generate effective number of test cases for identifying logical and semantic errors in the program. Basis path testing is a structural based testing technique whose main objective is to test each independent test path in the program at least once. It is very difficult for the tester of the software to test each generated test path therefore, optimization of test path is required to save time, cost and effort. This paper proposes a new meta-heuristic technique i. e Gravitational Search Algorithm (GSA) for optimization of test paths. The results obtained from newly generated meta-heuristic algorithm are compared with Simple Genetic Algorithm (SGA) and the results shows that GSA gives better results as compared to SGA.
International Journal of Information System Modeling and Design
Smart mobile pay applications on smart devices have been considered as the most efficient and sec... more Smart mobile pay applications on smart devices have been considered as the most efficient and secure mode of contactless payment. To safeguard customer credit/ debit card details, testing of mobile pay solutions like Samsung Pay is most important and critical task for testers. Testing of all the test cases is very tedious and a time-consuming task, therefore optimization techniques have been used to identify most optimized test paths. In this article, a hybrid genetic and tabu search optimization (HGTO) algorithm is proposed to secure optimized test paths using activity diagram of the smart Samsung Pay application. The proposed approach has been implemented using C++ language on the case study of the Smart Samsung Pay and an online airline reservation system. The experimental results show that the proposed technique is more effective in automatic generation and optimization of test paths, as compared to a simple genetic algorithm.
Software testing is the most important step for the development of software. Software programs ar... more Software testing is the most important step for the development of software. Software programs are executed against a number of test cases to check for efficiency and quality, by comparing actual output results with expected results. Model based testing allows the generation of test cases at early stage of software development even before coding. Early detection of faults during the software design phase reduces product developmental and testing costs. In this paper, we present a survey of various object oriented testing techniques which have been implemented for the generation of test cases, and for prioritizing the generated test cases using uml diagrams.
International Journal of Advanced Computer Science and Applications, 2011
Clustering and classification are two important techniques of data mining. Classification is a su... more Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database 'Fisher's Iris Dataset' containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.
Clustering and classification are two important techniques of data mining. Classification is a su... more Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database 'Fisher's Iris Dataset' containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.
Testing of software requires a great amount of time and effort. The tester's main aim is to d... more Testing of software requires a great amount of time and effort. The tester's main aim is to design optimized test sequences with a minimum amount of time, effort, and with less redundancy. Testers have used artificial intelligence meta-heuristic algorithms for optimization of test sequences. The model-driven approach is helpful in the generation of test sequences at early designing phase only. The model-driven approach uses UML diagram to represent the system's behavior and design test cases for the system at design stage of software development life cycle. The proposed approach uses natural river system for optimizing favourable non-redundant test path sequences using UML activity diagrams and sequence diagrams. The implementation of proposed approach has been done using python and results show that the proposed approach provides full coverage of test paths with less redundant test nodes compared to other meta heuristic algorithms.
Software complexity increases with the increase in functionality. To handle the complexity, unifi... more Software complexity increases with the increase in functionality. To handle the complexity, unified modeling language (UML) has been used to structure the designs. The model is further used as an input to generate optimized test paths. In this paper, a new test path optimization tool has been generated for the automatic generation and prioritization of test paths. In the first phase, an activity diagram has been created using “Visual Paradigm” software. It is then exported as an XMI file (XML metadata interchange). The aim is to generate adjacency matrix by parsing the XMI file. After creating it, the top-down approach is applied to generate the test cases. An algorithm called firefly algorithm is used to optimize and prioritize the critical paths. The priority to the test paths is assigned according to the values of information flow and cyclomatic complexities of each node. Using this algorithm, the redundancy of the tests paths was removed.
Testing of software requires a great amount of time and effort. The tester's main aim is to d... more Testing of software requires a great amount of time and effort. The tester's main aim is to design optimized test sequences with a minimum amount of time, effort, and with less redundancy. Testers have used artificial intelligence meta-heuristic algorithms for optimization of test sequences. The model-driven approach is helpful in the generation of test sequences at early designing phase only. The model-driven approach uses UML diagram to represent the system's behavior and design test cases for the system at design stage of software development life cycle. The proposed approach uses natural river system for optimizing favourable non-redundant test path sequences using UML activity diagrams and sequence diagrams. The implementation of proposed approach has been done using python and results show that the proposed approach provides full coverage of test paths with less redundant test nodes compared to other meta heuristic algorithms.
2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)
Storage provided by cloud is the valuable and significant utilization of cloud services for cloud... more Storage provided by cloud is the valuable and significant utilization of cloud services for cloud users. Cloud systems are useful for data sharing among the group and give a large number of benefits to users. Major characteristics of cloud computing includes integrity, confidentiality, privacy and accountability. It allows sharing of files between different users using cloud computing – based technologies. In this paper, we have categorized various cryptographic schemes for secure data sharing in cloud with their merits and demerits.
Data mining is essentially the discovery of valuable information and patterns from huge chunks of... more Data mining is essentially the discovery of valuable information and patterns from huge chunks of available data. Two indispensible techniques of data mining are clustering and classification, where the latter employs a set of pre-classified examples to develop a model that can classify the population of records at large, and the former divides the data into groups of similar objects. In this paper we have proposed a new method for data classification by integrating two data mining techniques, viz. clustering and classification. Then a comparative study has been carried out between the simple classification and new proposed integrated clustering-classification technique. Four popular data mining tools were used for both the techniques by using six different classifiers and one clusterer for all sets. It was found that across all the tools used, the integrated clustering-classification technique was better than the simple classification technique. This result was consistent for all t...
2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS)
Fundamentally, a sentiment refers to the reflection of emotions of people. Today's world stan... more Fundamentally, a sentiment refers to the reflection of emotions of people. Today's world stands on the strings of emotions. People express happiness, sadness, love, hatred etc. through some actions. Division of emotions i.e positive, neutral and negative, is called sentiment analysis. Nowadays there is a sentiment rich data in the form of tweets, status updates, blog posts, reviews, comments, forums for discussion etc. If we efficiently work upon this bucket full of sentiment rich data, it gives way in apprehending the opinions, views or perspective of masses in a specific functional area. Moreover, the result of this analysis will aid people in taking suitable actions or corrective measures for their growth. This effort of ours is like a drop in the ocean to try to analyze the reviews posted by people at four different websites (airlinequality.com, Amazon, Yelp, and IMDB). Further, the reviews are processed and analyzed using machine learning procedures, algorithms and other related aspects. Finally, the conclusion is derived by finding the polarity of a particular review whether it is poor, average or excellent for Airlines dataset and 0 or 1 for the other three datasets. The entire task was performed using Python.
Abstract Structural testing is used to test the internal structure or code of the program in whic... more Abstract Structural testing is used to test the internal structure or code of the program in which knowledge to the internal structure is must to generate effective number of test cases for identifying logical and semantic errors in the program. Basis path testing is a structural based testing technique whose main objective is to test each independent test path in the program at least once. It is very difficult for the tester of the software to test each generated test path therefore, optimization of test path is required to save time, cost and effort. This paper proposes a new meta-heuristic technique i. e Gravitational Search Algorithm (GSA) for optimization of test paths. The results obtained from newly generated meta-heuristic algorithm are compared with Simple Genetic Algorithm (SGA) and the results shows that GSA gives better results as compared to SGA.
International Journal of Information System Modeling and Design
Smart mobile pay applications on smart devices have been considered as the most efficient and sec... more Smart mobile pay applications on smart devices have been considered as the most efficient and secure mode of contactless payment. To safeguard customer credit/ debit card details, testing of mobile pay solutions like Samsung Pay is most important and critical task for testers. Testing of all the test cases is very tedious and a time-consuming task, therefore optimization techniques have been used to identify most optimized test paths. In this article, a hybrid genetic and tabu search optimization (HGTO) algorithm is proposed to secure optimized test paths using activity diagram of the smart Samsung Pay application. The proposed approach has been implemented using C++ language on the case study of the Smart Samsung Pay and an online airline reservation system. The experimental results show that the proposed technique is more effective in automatic generation and optimization of test paths, as compared to a simple genetic algorithm.
Software testing is the most important step for the development of software. Software programs ar... more Software testing is the most important step for the development of software. Software programs are executed against a number of test cases to check for efficiency and quality, by comparing actual output results with expected results. Model based testing allows the generation of test cases at early stage of software development even before coding. Early detection of faults during the software design phase reduces product developmental and testing costs. In this paper, we present a survey of various object oriented testing techniques which have been implemented for the generation of test cases, and for prioritizing the generated test cases using uml diagrams.
International Journal of Advanced Computer Science and Applications, 2011
Clustering and classification are two important techniques of data mining. Classification is a su... more Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database 'Fisher's Iris Dataset' containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.
Clustering and classification are two important techniques of data mining. Classification is a su... more Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database 'Fisher's Iris Dataset' containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values.
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Papers by Nisha Rathee