ability to generate new test cases based on existing test data. The expected output of the new test cases can be checked by using what we call metamorphic relations discussed in , . It proved to be efficient and effective in detecting most faults in a few seconds without the need of a human testing procedure. A metamorphic testing model introduced in  is another model for automatic generation of test data. Given a feature model and its known set of products, the algorithm generates neighboring models and their corresponding set of products. Generated products are then inspected to obtain the expected output of a number of analyses over the models. Extended Finite State Machines (EFSM) has proved to be a powerful approach not only in modeling and deriving test sequences but in the generation of test data as well. A technique that uses these machines is presented in , it considers any EFSM transition a function where the function name and input parameters are derived from the corresponding transition name and input parameters. Therefore, a set of inputs to be applied to a set of functions are called sequentially is considered a test data path. A fitness function is required to guide the search for a suitable set of inputs in this technique. State-based specifications are used to present general criteria for generating test inputs as introduced in . The technique parses specifications into a general graph called specification graph, then generates test requirements for a certain criterion or a set of criteria. It then generates for each test requirement test specifications that consist of prefix values, testcase values, verify conditions, exit conditions, and expected outputs to be used finally in generating actual test values that require solving algebraic equations.
Extended additional fault exposing potential (FEP) prioritization technique is the result of some modifications in additional fault exposing potential (FEP) prioritization. As in additional fault exposing potential (FEP), a term called confidence was used, in a very same way we also used this term. In this proposed technique, we use C(s)as a randomly generated value in our implementation. Let s denotes statement, t denotes testcase and FEP (s, t) denotes faults in s covered by testcase t, C(s) denotes confidence before execution of t and C‟(s) denotes new confidence after execution of t. Therefore, after this change in the value of C(s), equation here for the additional confidence in statement s becomes.
system consists of a collection of test cases, each of which is made up of the input of the program, called test data and the output that must be obtained. The need for increasing flexibility of industrial automation system products leads to the trend to shift functional behaviour from hardware solutions to software components. This trend causes an increasing complexity of software products and the need for comprehensive and automated testing approaches to ensure a requested quality level. Thus a testcase quality is bought out by using data mining algorithms. Genetic Algorithm method is used to improve the quality and reliability of the software by generating optimized test cases. And also one such popular data mining algorithm used in the project is CART (Classification and Regression Trees) algorithm. It is used in data mining with the objective of creating a model that predicts the value of a target based on the values of several input. CART uses a splitting criterion to test each data and produces a decision tree consisting of root node and child nodes. This is implemented to test the software and generates the testcase report. This system processes the developer to create a new software and provide it to the tester for generating testcase. The tester tests the software by undergoing all the levels of testing such as unit testing, integration testing, system testing, acceptance testing, alpha testing, beta testing. And the test cases are generated automatically for each test performed by the application. The generated test cases are viewed in reports which are specified in excel sheet and graph format. The report can be downloaded by both the developer and tester. And the tester application also does some process to identify number of bugs in the software, analyse most risk factor, and calculate time for completion of testcase report, and verifies the execution result. Thus the application generates a verified and assured testcase report.
It has already known that software testing is one of the most important and critical phase of software development life cycle assuring the verification and validation process of the software. Testing of software requires a great deal of planning and resources as it is a time-consuming activity. The software testing immensely depends on three main phases: testcase generation, test execution, and test evaluation. Testcase generation is the core of any testing process and automating it saves much time and effort as well as reduces the number of errors and faults. According to a survey on various object oriented testing techniques for generating effective test cases is presented. For example testcase generation using genetic algorithm, using UML sequence diagram, using UML activity diagrams, Scenario based testcase generation etc.
Abstract— Regression testing is retesting of a software system that has been modified to ensure that any bugs have been fixed and that no other previously working functions have failed as a result of the fixes and that newly added features have not created problems with previous versions of the software. Testcase prioritization techniques, which are used to improve the cost-effectiveness of regression testing, order test cases in such a way that those cases that are expected to outperform others in detecting software fault are run earlier in the testing phase. In this paper we are describing the test suite prioritization through fault exposed. As a result, this will help us to prioritize the test suite for execution and coverage.
The aim of master’s thesis was to determine prioritization of Testcase using Fuzzy logic based model. So as to get evaluation of Test cases in order of priority, Fuzzy based model was selected because of better decisions made by it in comparison to the additional normal expert systems. Moreover, Fuzzy logic allows the integration of numerical data and expert knowledge and can be a powerful tool when tackling significant problems in software engineering especially in testing environment such as determination of Testcase priority. In fact, the output of the proposed model is resulted from determination of Testcase priority order in the program MS Excel and Fuzzy Logic Toolbox in MATLAB. In order to fulfil the aim, firstly, it was essential to determine input variables along with parameters set to each Testcase and assigning its particular weights based on testing environment in BIAC’s company.
The overall testing process can be structured into the following central test activities: During testcase determination the input situations to be tested are defined. Concrete input values which meet the abstract test cases are determined during test data generation. For these test data the expected outputs are then predicted. The test object is run with the test data and thus the actual output values are produced. By comparing expected and actual values the test results are determined. Addition- ally, monitoring can give information on the behaviour of the test object during test execution. The most important prerequisite for a thorough software test is the design of relevant test cases, since they determine the kind and scope of the test.
Testcase generation the student imaging satellite has happened successfully. These test cases help to test the satellite in multiple scenarios. This facilitates testability, verification and correctness of the student satellite, which is made up of so many software components, but, here we generated test cases only for checking the functionalities of power-on initialization and data acquisition components. The future work deals with testcase auto generation. considering the functionality of each and every software component. It also enables to write test cases to test the conditions, path of execution of the software components and this is also useful in doing integration testing.
Investigating animal consciousness seems to be especially difficult since one can ask whether our usual concepts of human cognition should be applied to animals or whether our phenomenology can be used at all as a heuristic device. On the other hand the treatment of animal consciousness might be a testcase of various trade-offs and checks between, say, philosophical definitions of mental terms as to be applied to animals, neurophysiology, our reflected intuitions and ethological model building based on a computational theory of animal minds. For example the intuitive intentional description of a bug is given up as unwarranted anthropomorphic given that the bugs behaviour can be simulated by a little robot, which certainly is no intentional system. The neurophysiological guideline to look for human like neurophysiological structure excludes non-vertebrates as candidate for awareness but is disre- garded with respect to cephalopods since they exhibit intelligent behaviour (e.g. in a maze). Our men- tal terms as applied to humans and tied to the human phenomenology set the agenda for looking for animal cognitive abilities. There is, however, a first stumbling block on that road:
Requirement analysts possess relevant knowledge about the relative importance of requirements. After prioritizing requirements according to above metric, to further make the process interactive, an algorithm can be further introduced into this approach which considers second opinion from various experts to produce a requirement ordering which complies with the existing priorities, satisfies the technical constraints and takes into account the relative preferences elicited from the user. After considering the opinions from various analyst, a genetic algorithm can be applied where these opinions can be considered as the populations and a fitness function is calculated so as to obtain a final set of prioritised requirements. After the set of prioritised requirements is obtained, test cases can be ranked based on the degree how a particular testcase meets the requirements, the test cases that meet the requirements that appear early in the above obtained prioritised sequence are runned first following the ones that cover the requirements appearing lately in the requirement prioritisation sequence.
prioritization techniques can significantly affect the rate of fault detection of the test suite. The tester can choose to arrange the test cases in descending order of their priority values (with arbitrary ordering in case of ties).Hema  were interested in two particular objectives of testcase prioritization approaches: (a) to widen user professed software quality in a cost effective way by considering potential defect severity and (b) for improvement of the rate of discovering harmful faults during system level testing of newly generated code and regression testing of existing modified code. There is a simple approach for testcase prioritization which was proposed earlier through the requirement traceability matrix. The matrix can be produced by mapping from use cases in the Use Case diagram to functional requirements from users.
Software testing is very labor intensive task for developing software and improving its quality. According to some researchers and software professionals,50% of the time, cost and effort are spent on software testing.To test software, generating test cases is the most important task.Testing can be done either manually or automatically by using various testing tools. In today’s scenario software are testedautomatically with the help of tools as it is a fast and accurate process of testing software. Although various testing tools are available in market and are used by testers to test the software and to generate test cases and test data automatically.There are various techniques available for generating test cases like fuzzy logic, finite state machine, neural networks, genetic algorithms, soft computing, genetic programming, evolutionary computation and many others.This paper presents various testcase generation methods, testcase minimization, selection, and prioritization and evaluation techniques.This paper also focuses on various testcase prioritization and selection techniques that help the test engineers to schedule and rank the test cases to reduce the total effort, time and the cost.
statements, the probability of faults decreases. So, the next main motive should be the coverage of existing statements, so that the remaining faults can be revealed at faster rate and for the better and faster coverage of existing statements, we used genetic algorithm for the prioritization of remaining test cases in the proposed adaptive genetic approach, because according to Mark Harman, genetic algorithm gives the better code coverage and better fault detection rate in maximum cases than all other static coverage information based testcase prioritization approaches. ”
The AD is converted into an intermediate format called AG by using a mapping rule. While constructing the AG each activity of the AD is replaced by a node (one to one mapping) and an edge is assigned between two nodes of AG. In AG a node represents a state of doing something and an edge represents the flow between the activities. After constructing the AG the information about each node of AG is stored in a table called Node Description Table (NDT). The NDT maintain the information about each node of the AG, i.e. wether it is a fork node or a join node or a normal activity etc. After constructing the AG, the AG is traversed to generate the test cases. Every testcase is generated using some coverage criteria and are aimed to detect certain faults. Test coverage criteria are a set of rules that guide to decide appropriate elements to be covered to make testcase design adequate. In this approach Activity path coverage criterion is used. An activity path is a path in an AG which considers a loop at most two times and maintains precedence relationship between the activities. Each activity in AG is having at most one occurrences expect those activities which are in the loop, the activities in the loop are having at most two occurrences. Like every coverage criteria the activity path coverage criteria is aimed to detect three types of fault such as fault in decision, fault in loop, synchronization faults. Fault in a decision occur in the decision node of an activity diagram, for example in an activity diagram there is a decision node which decide the registration validity. Then there may situation where it may display the registration information of some registrant for some invalid registration id. Fault in loop occur in the entry or exit point of loop or increment, decrement operation. Suppose a loop is executed twice and at the end of iteration after giving try again = no, then instead of exiting from the loop the loop is executed for the third time. When some activity begins its execution before completion of execution of group of all preceding activities then synchronization faults occurs. Or simply synchronization faults occur when the concurrent preceding activities are not synchronized properly. The nonconcurrent activity path is used to find out the fault in the loop, and branch condition. And the concurrent activities are used to detect synchronized faults. Nonconcurrent activity path consist of set of sequential activities, concurrent activity path on the other hand consist of set of parallel activities. For generating test cases from the AG an algorithm is used called GenerateActivityPaths. The algorithm is a combination of DFS (Depth-First-Search), BFS (Breadth-First-Search). BFS is used to traverse the concurrent activities where as the rest activity are traversed using DFS. After applying the algorithm
The second drawback of the above solutions lies in the need for an explicit diffusion. For many numerical schemes, for example, finite volume or semi-Lagrangian schemes (Lin and Rood 1997), diffusion of small-scale features is implicit in the scheme: the inherent diffusion due to interpolation errors is often sufficient to prevent the build-up of enstrophy at small scales and to stabilize the numerical evolution. To compute the benchmark solutions of Galewsky et al. and Polvani, et al., such numerical schemes would be required to add an additional explicit diffusion term to the underlying equations of motion, inconsistent with the underlying model philosophy, and complicating the validation of the desired operational scheme. Indeed, this difficulty has led various groups to compute the Galewsky et al. solution without introducing explicit diffusion (e.g. Chen et al. 2013; Salehipour et al. 2013; Ullrich et al. 2014, among many others). While this is convenient numerically and may give a crude indication that the numerical scheme is performing more or less correctly, it prevents the testcase being used as a precise check of the numerical implementation and its accuracy. Thus, while the community clearly recognizes the importance for a refinement to the Galewsky et al. solution, so far none has been presented with a sufficient degree of rigour to enable accurate model validation (beyond being able to say that ones model is doing approximately the right thing).
In order to determine the readability of test cases are improved for that they compare both the testcases one with evosuite and another with EvoSuite Enhancer. They observe the major things from the test cases. In the experiment, for a Event class Evosuite generated more no. of test cases and test methods like test0(),test1() and soon. Initially participants were not able to understand quickly for which method the testcase is generated until only they will read entire code. And next, By adding comments to the test cases before declaration of the test class for example in
expansive method. Therefore the optimization of test cases is required and practically important. This process could be automated and less time consuming with perfection through hybrid intelligent technique. Improving the quality of generated test cases (especially in case of unit testing) automatically is a non-linear optimization problem. In order to tackle this problem, we have developed an algorithm called as OptiTest based on hybrid intelligence. The genesis of the algorithm is the implementation of ant colony and its internal pheromone distribution across the generated test graph. On the other hand the algorithm also incorporates another popular intelligent tool commonly known as Rough Set. From the perspective of search-based software engineering, the rough set based rule would like to denote the completion of search for optimized testcase. This novel hybrid metaphor has been generated test graph. On the other hand the algorithm also incorporates another popular intelligent tool commonly known as Rough Set. From the perspective of search-based software engineering, the rough set based rule would like to denote the completion of search for optimized test cases.
ABSTRACT: one of the major quality criteria of a software system is how well it fulfills the needs of users or customers. One technique to verify and improve the grade of fulfillment is system testing. System test cases might be derived from the requirements of the system under test. The software testing immensely depends on three main phases: testcase generation, test execution, and test evaluation. Testcase generation is the core of any testing process; however, those generated test cases still require test data to be executed which makes the test data generation not less important than the testcase generation. This kept the researchers during the past decade occupied with automating those processes which played a tremendous role in reducing the time and effort spent during the testing process. This paper explores different approaches that had regarding the generation of test cases using UML models.
Testcase prioritization techniques have been focused on regression testing which is conducted on an already executed test suite. In fact, the testcase prioritization for new testing is also required. In this paper, we propose a method to pri- oritize new test cases by calculating risk exposure value for requirements and analyzing risk items based on the calcula- tion to evaluate relevant test cases and thereby determining the testcase priority through the evaluated values. Moreover, we demonstrate effectiveness of our technique through empirical studies in terms of both APFD and fault severity.