Weighted random pattern generation methods relying on a single weight assignment usually fail to achieve complete fault coverage using a reasonable number of test patterns since, although the weights are computed to be suitable for most faults, some faults may require long test sequences to be detected with these weight assignments if they do not match their activation and propagation requirements. Multiple weight assignments have been suggested for the case that different faults require different biases of the input combinations applied to the circuit, to ensure that a relatively small number of patterns can detect all faults. Approaches to derive weight assignments for given deterministic tests are attractive since they have the potential to allow complete coverage with a significantly smaller number of test patterns. All weighted random and related methods suffer from the following limitation. To produce weights which are different from 0.5, several cells of an LFSR or a shift register are connected to a gate whose output is used to derive the corresponding primary input of the circuit under test; e.g., to produce a weight of 0.25, two cells of the LFSR are connected to an AND gate, whose output drives a primary input of the circuit. When weights are allowed to assume arbitrary values, arbitrary numbers of shift-register cells have to be used to produce the required input values. Register cells are generally not allowed to be shared between circuits that generate weights for different primary inputs, to avoid correlation between the values different primary inputs assume. In this paper, we propose a method that dynamically walks through the range of possible testgeneration approaches, starting from pure random tests to detect easyto- detect faults at low hardware cost, then reducing the number of inputs which are allowed to be specified randomly, fixing an increasing number of the inputs to 0 or 1 according to a given deterministic testset, to detect faults which have more stringent requirements on input values and cannot be detected by a purely random sequence of reasonable length, finally allowing deterministic tests to be
Given the large domain of inputs and possibly too many possible execution paths, the software is often tested using a sampled set of test cases. A variety of coverage criteria have been proposed to assess the effectiveness of the sampled set of test cases. As far as structural testing involving predicate evaluation is concerned, criteria exercising aspects of control flow, such as statement, branch and path coverage have been the most common. Although useful, these criteria are often susceptible to the problem of masking. Addressing this issue, this paper explores to adoption of mc/dc as the necessary criteria for structural testing.
Software testing is becoming more complex day by day. This complexity enforces using techniques and methods to assure software quality. One of this methods is system testing. The main goal of system testing is to verify that requirements are successfully implemented into system under test. In order words, system testing assures that software system does what it is expected to do. Model based testing (MBT) refers to the type of testing process that focuses on deriving a test model using different types of formal ones, then converting this test model into a concrete set of test cases , , . Those formal models have many different types, but all of them are generally categorized into three main categories: requirements models, usage models, and source code dependant models. The requirements models can be behavioral, interactional, or structural models according to the perspective by which the requirements are being looked at. The test cases derived from behavioral or interactional models are functional test cases and they have the same level of abstraction as the models creating them. These kinds of test cases differ from those derived using structural models. Other types of models can be used as well to extract test cases , , , ; The Unified Modeling Language (UML) models are considered one of the most highly ranked types being used -.
NSGA-II uses a set of genetic operators (i.e., crossover,mutation, and selection) to iteratively evolve an initial population of candidate solutions. In our case, candidate solutions are test cases orderings. Evolution is guided by an objective function (i.e., the fitness function) that evaluates each candidate solution along considered dimensions. In each iteration, the Pareto front of best alternative solutions is generated from evolved population. The front contains the set of non-dominated solutions, i.e., those solutions that are not inferior (dominated) to any other solution in all considered dimensions. Population evolution is iterated until a (predefined) maximum number of iterations is reached. In our case, a Pareto front represents the optimal trade-off between the three dimensions determined by NSGA-II. The tester can then inspect a Pareto front to find the best compromise between having a test
Software testing is most essential and integral activity in software development life cycle. This activity is performed by using a systematic approach .The testing process is a method of executing a program on a set of test cases and comparing the actual results with the expected results. Test cases are usually derived from software artifacts such as specifications and design. Testers initially go through the requirement document to understand requirements and specifications. After this is done, they start preparing test cases using testcase template. A basic testcase template should have details like Test Steps, Test Data, Expected Result, Actual Result, Pass/Fail and comments. It should have other details like Pre- conditions, assumptions, requirement numbers, date, tester name etc. Test Cases are generated automatically using testing tools OR testers have to manually write test cases. There are various TestCaseGeneration techniques used to generate test cases. The two main approaches are given [R.Mall] to automate the test cases. The first approach is to designing the test cases from requirements and design specifications. The second approach is to design test cases usingcode. Although generating test cases from coding is quite complex task therefore the first approach is mostlyadopted. This process also helps the test engineers in findingand analyzing the problems and faults with the designed system. The purpose of this paper is to study the testcasegeneration techniques by using the various existing techniques. The various phases of testcase life cycle are Testcasegeneration, testcase selection, testcase minimization, testcase prioritization and evaluation.
After consideration of the above known facts about each crossover scheme, two point crossover was implemented within GA-MITS with pc equal to 95%. Two point crossover seems superior to single point crossover in this application because of its reduced end point bias and the lower likelihood of long schemata disruption. Having examined the results of GA-MITS there are many cases where the fittest chromosome contained schemata o f large defining length. Although uniform crossover offers the advantage of being able to recombine all present schemata, it can be very disruptive especially for genes that coexist and which are essential for test sets of minimal size. This phenomenon of genes that coexist is known as coadaption in evolutionary terms . For example, i f alleles values of 1 are essential for a minimal testset at loci n and n+1 (where l< n + l< iV (r )max) then there is a higher probability of this schema being disrupted using uniform crossover than for both single and two point crossover. Also in the case of a minimal testset containing unique patterns (vectors), the allele o f 1 at the corresponding loci will also be a coadapted gene along with all the other necessary test vectors. Since 10 o f the minimal test sets located by GA-MITS were proven as being global minima, it suggests that the phenomenon o f the unique patterns, and hence o f the coadapted gene(s), is frequently encountered within testset minimisation problems. It was for this reason and the slow convergence rate that a uniform crossover scheme was dismissed for use in GA-MITS.
M. Sarma et al. presented an approach of generating test cases from UML design diagrams. A UML use case diagrams transformed into a graph called use case diagram graph (UDG) and sequence diagram into a graph called the sequence diagram graph (SDG) and then integrating UDG and SDG to form the System Testing Graph (STG). The STG is then traversed to generate test cases for system testing. They have used state-based transition path coverage criteria for testcasegeneration. Having stored all essential information for testgeneration in the STG, they now traverse the STG to generate test cases. The Test Suite Generation algorithm, traverse the STG at 2 levels. The traversal begins with the UDG. This traversal visits all use cases and generate test cases for detecting initialization faults. At level 1, if a use case initialization faults occur then it was assume faults in its operation and therefore no need to apply test cases corresponding to the operation. At level 2 traversal, starting from a use case node the corresponding SDG was visited and test cases were generated to detect operational faults . Chen et al. proposed a technique in which they use UML activity diagrams as design specifications, and consider the automatic approach to testcasegeneration. Instead of deriving test cases from the UML activity diagram directly, they presented an indirect approach which selects the test cases from the set of the randomly generated testcase according to a given activity diagram. In this method, they first randomly generate abundant random test cases. Then, by running the program with these test cases, they will get the corresponding program execution traces. Last, by comparing these traces with the activity diagram according to the specific coverage criteria, they can prune some redundant test cases and get a reduced testcaseset which meets the test adequacy criteria.
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.
diagram doesn’t have the time as a separate dimension so the messages have ordered using sequence number in edge. For example if the first message passed from the object A to B then it is numbered as 0. Then if the next message sequence is passed from B to C then it is numbered as 1. Suppose after that there are two messages from object C then one of them will be ordered as 1.1 and the other will be ordered as 1.2. While constructing the communication tree the edge plays a major role. Since edge represents the message sequence along with the message. So considering the message sequences the communication tree is constructed. The edge, which initiates the message sequences having sequence number 0, is made as root node of the communication tree. Next the edge having sequence number 1 becomes the child node of node 0. And if after that there are two edges numbered as 1.1 and 1.2 then the node 1 will have two children node one will be 1.1 and the other will be 1.2. After constructing the communication tree their next step is now to traverse the communication tree to select the conditional predicate. This technique traverses the Communication tree in post ordered manner. While traversing the tree in post order for selecting the condition predicate the predicate which are in the leaf node will be selected first. After selecting the conditional predicate, functional minimization technique is applied. If the predicate is of the from (E1 op E2), where op is ≤, <, ≥, > then F (predicate function) = (E1-E2) or (E2-E1) depending upon which is positive. This process is done to test the boundary testing. The final step is to generate the Test Data. While generating the test data one set of data should be generated so that the predicate function becomes true and another set of test data should be generated for which the predicate function becomes false. In this step while generating the test data the advantage of post order traversal process comes. Since this technique first takes the leaf node conditional predicate so while generating test data for a predicate function if any pre condition path is there then it should satisfy that resulting less number of test data.
In our approach, modeling the GUI and the application behavior will not involve making a model and using all the GUI elements and producing a large amount of test cases coveringall the possible event modules. It will work by defining a set of test cases and representing the most crucial GUI elements to include both interesting values and a set of validation rules so that we can support the testcase auto generation process. It is also not necessary to manually verify, fix, or complete any model in this approach, which removes this error-prone process from the GUI Testing process and makes the work easy for the testing team. These features will help to improve the maintenance of the software system.
Abstract: In software testing is an crucial part of software development in the number of verification and validation process of the software. In an object oriented method we must have to apply the method of mapping the software for all its transition states and the number of the output for a set of given input. For a any given part of software we will be writing a set of test cases that called test suites and test place it is used to group together similar test cases. Test suites is a collection of test cases that are planned to be used to test an object oriented method to illustrate that it has some specific set of behaviors. In order to find out how a testcase is valid or not for that we do not have specific mechanism. We mostly depend on the software testers understanding of the requirement. In my paper study UML diagrams different technique by using use case in test cases, for example testcasegeneration using testcasegeneration using random based testing, testcasegeneration using Model based testing. The test cases are derived by analyzing the dynamic test of the objects due to external and internal stimuli.
In above C-Language code we can calculate APFD for different test suite and compare the results for testcase prioritazation .tn is number of test cases ,fn is number of faults ,tsn-number of testcase in test suit. fault found by a testcase represent show by 1 value in corresponding row and column oterwise it represent 0 value .All 0 and 1 value stored in two dimensional array.
2.4 Test cases with improved Readability Evosuite Enhancer performs two tasks and it will be the output of this step. The modifications are done in the previous step that will be collected and modified to the original test suite. The new test suite will be more readable and easy to understand. It will take less time to read so reduce the effort and cost of time to developers. 3.EXPERIMENT METHODOLOGY The goal of this experiment is to investigate how easy to understand the generated tests before and after an approach is used. This section describes the experimental setup and procedure for the experiment in detail, following previous reporting guidelines for empirical software engineering research.
Our future work includes the following: First, we plan to investigate a new testcasegeneration technique with high path coverage. We can adapt symbolic execution approach combined with con- crete execution  or infer the input values from existing manually-created test cases to reduce false negatives. Second, we plan to apply our approach for other types of security vulnerabilities. Our approach can be applied to the vulnerabilities with known hotspots where string type user input is used. Third, we plan to provide test coverage information to give high confidence on the testing results. Fourth, we plan to construct attack pat- tern database with more thorough attack input in a way new attack pattern can be easily added and used to generate test cases.
Problems exist in non-deterministic execution in testing concurrent programs. The process to produce the test cases will not hit the problem of state explosion. The numbers of test cases are decreased as the co paths gener- ated are made up systematically. The method however in practical in the sense that the number of task instances is produced from task-type is restricted and the co paths are also modified. The TCgen tool produces co paths from a concurrent program which includes any task-type. The number of overlapping test cases is decreased as the co path generated through the algorithm is made logically.
Several experimental studies have been carried out on various automated testcasegeneration techniques. This section presents the methods and results of some similar work that have been carried out on automated testcasegeneration. Reference  conducted experiment on four test data generation techniques (Random technique, IRM based Method, Korel method and GA based method). The results of the experiment show that the genetic algorithm (GA)-based test data generation performs the best. Reference  carried out an experiment comparing a total of 49 subjects split between writing tests manually and writing tests with the aid of an automated unit testgeneration tool, EVOSUITE. The purpose of this study was to investigate how the use of an automatic testgeneration tool, when used by testers, impacts the testing process compared to traditional manual testing. Their results indicated that while the use of automated testgeneration tools can improve structural coverage over manual testing, it does not appear to improve the ability of testers to detect current or future regression faults. Reference  compared the effectiveness of Concolic testing and random testing. The experiment shows that Concolic testing is able to find significantly more bugs than random testing in the testing domain. Reference  presented an empirical comparison of automated generation and classification techniques for object oriented unit testing. Pairs of test-generation techniques based on random generation or symbolic execution and test-classification techniques based on uncaught exceptions or operational models were compared. Their findings show that the techniques are complementary in revealing faults. Some other experimental studies conducted are on the evaluation of tools , .
Care must be taken in interpreting a waterfall image such as that shown in Figure 1. Pixels in this image cannot be interpreted as having a real and consistent physical scale. Turns of the vehicle or alterations in its velocity while sampling will yield increased distortion which will impair any type of manual or automated interpretation. There are a range of corrections and procedures required to generate images whose pixels can be interpreted as having a real physical scale and which are free of distortions induced by the motion of the vehicle. Some researchers have attempted to apply automated techniques to waterfall images [Stadler et al., 2008] but we believe these techniques would be more effective on corrected images. One of the contributions of this work is the image generation procedure which carries out the necessary corrections and
If no test can detect faulty, it is called an undetectable fault. Such a circuit with the undetectable fault is called a redundant circuit. The most attractive feature of such a redundant circuit is its fault prevention property. According to prevention property, if a circuit has detectable faults, in the meanwhile another fault occurs in the circuit there may be a possibility that faults may diminish due to the presence of another fault and the output remains the same as the expected output. Undetectable fault sometimes may be good in the circuit due to its prevention property. For example, there is a circuit as follows in fig4 in which testset is assumed as (1101). Testset (1101) gives the output 1. We assume input b stuck-at-0. Therefore the input sets become (1001), and it gives the output 0. It means the circuit is faulty because it is not as the same as expected output 1. At the same time, we induce another fault at c is stuck-at-1. Now the assumed testset will become (1011), and it will give the output 1. The Fault is now no longer detected. We can state that the redundant circuit can always be simplified by getting rid of any gate or gate inputs. The detailed rules for building the circuit undetectable is defined in table1.
The reason for the hesitant adaption are most likely rooted in the dominant programming standard IEC 61131-3. Even though there are many similarities between programming in the domains of computer science and production automation, most of the presented concepts are not directly applicable to automated production systems’ program code: The IEC 61131-3 consists of different graphical as well as textual programming lan- guages and exhibits differences regarding structure and behavior that make an adaptation complicated. Ad- vances towards object-orientation are being made, yet the structure of IEC 61131-3 programs is neither com- pletely object-oriented (e.g. many global accesses), nor procedural (e.g. class like function blocks). All of the analyzed approaches are directly aimed at one of these paradigms. Regarding behavior, cyclic execution of the code significantly influences the programming paradigm, and thus hinders a direct application of the approaches which usually assume a single execution of the program per testcase. The test cases used in the publications are designed accordingly: single input vector test cases that do not allow adequate testing of state machines, which are common in automated production systems.
A new approach for automated test data generation is evaluated the name of which is Big Bang-Big Crunch algorithm is used for testcasegeneration. Static testing based symbolic execution method has been used in which first, target path is selected from CFG of program and then inputs are generated using the BBBC method to satisfy composite predicate corresponding to the path. It has been observed that the BBBC method is better alternative than random testing.