used to find a pattern like class name, class attributes, class cardinality, class operations, inheritance, dependency etc. 3) Is pattern found: If pattern has been found then it is entered to the queue else next pattern has been found. 4) Store it in a queue: All patterns found have been stored in a queue. There are different queues for every pattern like class name queue, class attributes queue, class cardinality queue, class operations queue, inheritance queue, dependency queue etc. 5) Search for another pattern: Tool searches for various patterns in petal file. For example if class name has been found then class name has been entered into the class name queue Else if class attributes has been found, then tool enters it to the class attributes queue similarly so on. 6) Is EOF (End of File): Tool keeps on searching the patterns until EOF? 7) Create text file from queue and store text file in database: When end of file has been reached, the tool generates the text file which contains all information about class diagram in form of tuples which can be entered to the database easily using SQL *Loader. SQL *Loader (Oracle load data files feature) loads all the data from the text file to the oracle database. 8) Are all Petal files input: After class diagram, Petal files of sequence diagram and statechart diagram have been entered to the tool. 9) Retrieving strings to generate test cases: Oracle database which contains the information from class diagram, sequence diagram, statechart diagram has been used to generate test cases. Tool has been
Test data generation research are going on since 1970's. But unfortunately till today there is hardly any fully automatedtest data generation tool found in industry. Initially people did research on test data generation using symbolic execution in 70's upto mid 80's [62, 70]. At that time the language taken for test data generation was FORTRAN Algorithm . In 1987 Parther  had contributed a new idea for test data generation called path prefix method. In 1990, B. Korel had made a revolutionary change by generating test data dynamically based on actual value using pattern and explanatory search. In 1996 Korel  developed assertion oriented and chaining approach  Goal oriented test data generation. In 2000 test data generation on dynamic data structure is emphasized[12, 26, 57, 42, 43]. Mahmood in  has given a good review of test data generation techniques from 1997 to 2006. But the paper ignore technical details of the methods found in his reviewed paper. During 2004 to 2006 clever implementation of random testing is done to get the benefits of avoidance of infeasible paths and to ignore path selector module[37, 67, 31]. During this time test data generation using hybrid method that takes the advantages of both static and dynamic method were done [28, 66]. The William work path crawler has the advantages because it ignores infeasible paths. But problem is of exponential increase of number of paths. In 2000 there are many other papers who worked to detect infeasible paths for saving computational time [18, 54]. In 2005 Chen etal  had shown how to implement automatedtest data generation for teaching students. This type of work is very useful for beginners to know how to start research. In 2010 Tahbildar etal  has given a heuristic to determine the number of iteration required
Test case generation for various aspects in incorporating test cases covering various factors such as usability, scalability, network connectivity and internet protocols is a difficult task due to the distributed and heterogenous nature of IoT systems Since the IoT devices are made of different hardware and technologies so there are always difficulties in testing them. There are many critical bugs related to functionality, performance, and security. Since IOT is evolving at a faster rate so the quality of the software in IoT devices cannot be compromised. The users of the connected devices are unaware of how the IoT system is working but are very much accustomed to using IoT technology. Even though there are several tools available that can be used in the validation of IoT components, there occurs number of issues that can be addressed: a technological review of existing solutions reveals the lack of a comprehensive test solution for automated integration testing. Focusing on a specific protocol, network, or standard, decreasing the feasibility of upgrading or extension, and not providing out-of-the-box functionality are among the most common shortcomings detected. Alerts are triggered when the patient’s status is validated to require immediate attention and pre-set operations are generated by the variations in the abnormal conditions.
A Feature Specification Document, written by the systems engineering organization, details the requirements for the behavior of call processing features of the 5ESS. The behavior of the feature depends on inputs from the parties on the call and the configuration and signaling input from the 5ESS network. Complex interactions arise between the calling parties, other features on the switch, and the network, and these must be understood to adequately test the new feature. To date, testgeneration has relied on manual methods to interpret the Feature Specification Document, state diagrams, and call processing behavior of the switch. For a given call, the switch waits for input, e.g., a set of DTMF tones. The switch processes the input and changes the state of the call in progress. (Different inputs from the caller and network configurations cause the 5ESS switch to process calls differently.) For example, if the user enters a valid telephone number, the call will be processed; if not, an announcement will play asking for a valid input. Advanced features in the 5ESS switch have so many variables that it is difficult for the test engineer to identify them all, let alone generate a set of tests to verify that the feature works in all cases.
Suppose that the set of transparent-scan sequences Tsel2 is selected for the group, and the subset Tsel2,i⊆Tsel2 is selected for testing Bi . Considering a transparent-scan sequence Tsel2,i, the contribution of Tˆi, ˆj to the test application time for Bi is approximately kˆi. This is the number of state variables of the logic block Bˆifor which Tˆi, ˆj was generated. If the scan-in operation of Tˆi, ˆj is overlapped with the scan-out operation of the previous test, Tˆi, ˆj adds kˆi+ 1 clock cycles. This information is shown in column applic of Tables VIII and IX for the logic blocks in some of the groups. Sub column S shows the estimated test application time for the conventional scan-based test set of each logic block. Subcolumn Tsel2 shows the estimated test application time for the transparent-scan sequences that are selected for the logic block by the static test compaction procedure. Considering a logic block Bi , the test application time for Bi starts from a value that is close to that of the conventional scan-based test set when Bi is one of the largest blocks in the group. The test application time for Bi increases as additional logic blocks are added to the group. The increase occurs since the new logic blocks that are added to the group have more state variables than Bi , and their transparent-scan. sequences have a higher contribution to the test application time when they are selected for Bi However, the highest test application times are obtained for the largest blocks in the group, and these test application times are close to the test application times for the conventional scan-based test sets. These are expected to dominate the test application time for the group as a whole.
1 INTRODUCTION OFTWARE is advanced in recent days by enhancing its applicable domains. Software is embedded in almost all electronic gadgets and systems. In this scenario the quality of the software plays a significant role. The customer or end – user should be satisfied which is primarily depended on quality and capability of the software being developed. In order to ensure quality of the software, software organizations perform testing. Software Testing  is a quality control activity which involves defect detection and correction. Software Testing is a part of SDLC process to validate and verify the working of a developed product or application . Testing can be performed at various stages of software development process depending upon the methodology and tools being used and usually begins after the requirement confirmation phase. The initial phase is at unit level where it mainly focuses on coding. When coding is completed, we perform Integration testing for finding out the bugs in the software application. It helps in preventing the software failure. The ultimate purpose of software testing is to satisfy the stakeholders as well as ensuring the quality of the application. Software industry usually follows automated testing than that of manual testing . In manual testing, testers evaluate the software manually for the faults. Tester behaves like an end user and evaluates all the possible features and functionalities of the developed software for ensuring its behavior and quality. The tester manually prepares test plan  and suitable test cases which is tested over the application to verify the behavior of Graphical User Interface (GUI) , functional and non – functional requirements. But performing manual testing requires a large amount of human intervention and the presence of an experienced - skilled person to design an appropriate test suite. In automated testing, execution of test cases is supported by automation tools. Automated testing is quite beneficial in case of large projects.
Abstract-This paper proposes a technique to generate the multiple test patterns varying in single bit position for built-in-self- test (BIST). The conventional test patterns generated using LFSR have an absence of correlation between consecutive test vectors. So, in order to improve correlation between the subsequent test vectors, test patterns were produced using binary to thermometer code converter. The methodology for producing the test vectors for BIST is coded using VHDL and simulations were performed with ModelSim 10.0b. 100% fault coverage is achieved with less number of test patterns. The Area utilization, power and delay report were obtained with Xilinx ISE 9.1 software. The area reduction of 62%, power reduction of 13% is achieved while generating test patterns using binary to thermometer code converter when compared with the patterns generated using Reconfigurable Johnson counter and LFSR.
This paper is divided into four sections. The first section discuss about the fault diagnosis in scan chain using Jump simulation . The second section discuss about the testpatterngeneration to carry out Jump simulation. The third section discusses the results and discussions. Finally, the last section is a brief conclusion about the work done.
The second class is low power TPGs. Wang and Gupta used two LFSRs of different speeds to reduce the frequency of transition at the circuit inputs, leading to reduction in switching activity during test application . Corno et al. provided a low power TPG based on cellular automata to reduce test power in combinational circuits . The scheme in  is the approach focusing on modifying LFSR. Modified clock schemes reduce the power in the CUT. In , a low power BIST for data path architecture is proposed, which is circuit dependent. Bonhomme et al.  used a gating technique where two non overlapping clocks control the odd and even scan cells of the scan chain so that the shift power dissipation is reduced by the factor two. The ring generator  can generate a single input change (SIC) sequence. This method compares to the corresponding already known TPGs with respect to the fault coverage obtained by the test sequence of same length.
Abstract: Current software industry‘s prime focus is on developing quality application programs. The basic business management principle ‗Customer acceptability is directly proportional to the quality of the product‘ is admissible for software products too. Thus, testing phase has a vital role in improving customer satisfaction of a software application. The various research analytics claim that nearly 30% effort of entire software development is used for performing testing activities. Every software firm or application developers follow a typical custom set of testing strategies and uses some standard testing tools for quality assurance. The project manager has to decide the testing strategy between manual and automated testing. In automated testing, there are many tools available with different capabilities and performance characteristics. This review analyzes the performance metrics of various testing tools and testing strategies used for enriching the quality of the application being developed. The review result may guide the project manager to make the trade-off decisions for choosing the appropriate testing tools and testing strategies applicable for their project domain.
of its space-efficient representation, its heuristic search and simple, straightforward approach to handling the environment. The automatedgeneration of test suites with a high coverage on a diverse set of real, complicated, and environmentally-intensive programs is also the strength of KLEE. For evaluation purpose, KLEE is applied into all 90 programs in the latest stable version of GNU COREUTILS. In recent studies, KLEE is used in a variety of areas including wireless sensor networks, automated debugging, reverse engineering, testing of binary device drivers, exploit generation, online gaming and schedule memorization in multithreaded code.
An animation system that generates an animation from natural language texts such as movie scripts or stories was developed. The system does semantic analysis to find the motion clips based on verbs . Invention relating to the creation of computer database systems and the querying of data contained therein was done in . The steps involved generation of fact tree based on natural query, check query for semantic correctness and generate query for the database. Conversion of business rules written in natural language to set of executable models as UML, SQL etc. was carried out in .
Genetic Algorithm has low search efficiency in later period of revolution. In order to overcome such shortcoming of Genetic Algorithm, researchers introduce some algorithms which have better effect on search, such as Annealing algorithm and Ant Colony Optimization [4, 13-16]. As a result, they produce some new algorithms such as Mixed annealing genetic algorithm and Hybrid genetic ant colony algorithm. In field of test, researchers have already used Mixed annealing genetic algorithm and Hybrid genetic ant colony algorithm to test the result of experimental data. Generally speaking, Hybrid genetic ant colony algorithm has a good effect of restraining local convergence and improving the search efficiency. In fact, Hybrid genetic ant colony algorithm itself also has such characteristics, early maturity in local convergence and low effect in searching. Anyhow, Hybrid genetic ant colony algorithm is better than Genetic Algorithm. Ant colony system algorithm is put forward to overcome the shortcoming of ant Colony Optimization. Many literatures illustrate that ant colony system algorithm is more competent and efficient in local search. In this paper, we introduce genetic algorithm to ant colony system algorithm and form a mixed algorithm which carries on and improve the advantage of the two combining algorithms, called ACSGA. In order to test its adaptability, we choose classical triangle discrimination problem used frequently in the experiments of path-oriented software testing to verify the efficiency of ACSGA. GA has started getting competition from other heuristic search techniques, just like the particle swarm optimization (PSO). Like GA, PSO is set with a population of random solutions. The development was based on survey of the social behaviour of animals such as bird flocking and swarm theory. Each one in PSO is assigned with a randomized velocity according to its own and its companions‟ flying experiences, and the individuals, that are called particles, are then flown by hyperspace. On Comparing with GA, PSO has few attractive characteristics. It has memory, so the knowledge of right solutions is retained by all particles. Since in GA, above knowledge of the problem is destroyed once the population changes. It has useful cooperation between particles, the particles in the swarm share information between them . Various works – show that particle swarm
There are two ways of performing mutation using the Major mutation framework, but I chose to integrate the process using Apache Ant. The main reason behind using the Apache Ant is its ease of use and using Apache Ant allowed me to use the same build.xml script to integrate Cobertura , which is the code coverage tool employed in the experiment. After integrating both Cobertura and the Major mutation framework, we can perform mutation and coverage analyses. The coverage that is obtained using cobertura when it is integrated with Apache Ant is statement/line coverage and branch coverage. Apache Ant is run from the command line and it is run according to the targets available in the build.xml file. I wrote a bash script for specifying the targets without retyping every command on the command line. The Bash script was written to compile the source code and generate the bytecode of the source from which mutation scores and coverage are calculated. All the bytecode of the source is stored in the “bin” directory (executables for major components). Initial source code for the target is stored in the “src” directory and the test cases are stored in the “test” directory. All the mutants that are generated during this step are stored in the “mutans.log” file. Apart from the mutants generated, there is a list of other important files used for analysis. Here is the list of the files generated during the process:
In order to overcome this problem, an accumulator-based weighted patterngeneration scheme was proposed .the scheme generates test patterns having one of three weights, namely 0,1,0.5 therefore it can be utilized to reduce the test application time in accumulator-based testpatterngeneration, However, the scheme proposed three major drawback: 1) it can be utilized only in the case that the adder of the accumulator is a ripple carry adder; 2) it requires redesigning the accumulator; this modification, apart from being costly, requires redesign of the core of the datapath, a practice that is generally discouraged in current BIST schemes; and 3) it increases delay, since it affects the normal operating speed of the adder.
This paper introduces the function of test cases with minimal power for Built-In-Self-Test (BIST) implementation. This method intends Test-Per-Scan (TPS) based test cases using Multiple Single Input Change (MSIC) architecture. Multiple SIC patterns are developed by using EX-OR operation of twisted ring counter and test design algorithms like Linear Feedback Shift Register (LFSR),Bit-Swapping LFSR (BSLFSR), and Cellular Automata (CA). These patterns are used to a diminish number of transitions in the test patterns that are generated. The preferred method uses Test-Per-Scan technique for generating Multiple SIC test patterns. TPS diminished the power consumption during test mode. The seed generator used in TPS is modified LFSR ’ s i.e., BS-LFSR, Cellular Automata (CA). BS-LFSR is composed of with an LFSR with a multiplexer. In CA, it also presents a variation on a BIST technique, which is from a one-dimensional cellular automaton; the pseudo random bit generator is generated. The proposed Hybrid Cellular Automata (HCA) using the rules 90 and 150 to generate the pseudo random designs. Moreover, the CA implementations illustrates properties of data compression like LFSRs and that they exhibit locally and with topological consistency significant attributes for a VLSI design. In this proposed method, LFSR is replaced with BS-LFSR, and HCA. Simulation and synthesis outcome with ISCAS c432 benchmark determine that Multiple SIC can reduce the power consumption.
The third class makes use of the prevention of pseudorandom patterns that do not have new fault detecting abilities –. These architectures apply the minimum number of test vectors required to attain the target fault coverage and therefore reduce the power. However, these methods have high area overhead, need to be customized for the CUT, and start with a specific seed. Gerstendorfer et al. also proposed to filter out non detecting patterns using gate-based blocking logics , which, however, add significant delay in the signal propagation path from the scan flip-flop to logic. Several low-power approaches have also been proposed for scan-based BIST. The architecture in  modifies scan-path structures, and lets the CUT inputs remain unchanged during a shift operation. Using multiple scan chains with many scan -enable (SE) Inputs to activate one scan chain at a time, the TPG proposed in  can reduce average power consumption during scan - based tests and the peak power in the CUT. In , a pseudorandom BIST scheme was proposed to reduce switching activities in scan chains. Other
Very Large Scale Integration (VLSI) has made a dramatic impact on the growth of integrated circuit technology. It has not only reduced the size and the cost but also increased the complexity of the circuits. The positive improvements have resulted in significant performance/cost advantages in VLSI systems. There are, however, potential problems which may retard the effective use and growth of future VLSI technology. Among these is the problem of circuit testing, which becomes increasingly difficult as the scale of integration grows. Because of the high device counts and limited input/output access that characterize VLSI circuits, conventional testing approaches are often ineffective and insufficient for VLSI circuits. Built-in self-test (BIST) is a commonly used design technique that allows a circuit to test itself. BIST has gained popularity as an effective solution over circuit test cost; test quality and test reuse problems. In this paper we are presenting an implementation of a tester using Verilog. Test time is a significant component of IC cost. It needs to be minimized and yet has to
This section explains how test compaction occurs in a high-level ATPG using Chen’s functional fault model. Chen’s fault model defines several types of faults as elaborated in Section 3, most of which are injected at the input/output of the modules in a circuit described at functional level. These functional faults are mapped to the gate-level stuck-at faults at inputs of a module. Be- sides that, we also analyze that micro-operation faults correlate with some stuck-at faults. The correlation con- tributes to gate-level test compaction, which can be proved by checkpoint theorem theoretically. Checkpoints are defined as primary inputs and fan-out branches of a circuit which have been proposed as starting set of faults for both equivalence and dominance fault collapsing .
A common objective of testing is to detect all or most modeled faults. Although fault coverage has a somewhat nonlinear relationship with the tested product quality or defect level (parts per million), for practical reasons fault coverage continues to be a measure of the test quality. The increase in the design complexity and reduced feature sizes has elevated the probability of manufacturing defects in the silicon. These defects could result from shorts between wires/vias, breakage in wires/vias, transistor opens/shorts, etc., Fault diagnosis is the process of finding the fault candidates from the erroneous response. Any vector that can produce different responses for two different faults is called a distinguishing vector for those faults. Hence, to reduce the number of fault candidates, a test set that is able to distinguish between all distinguishable faults is highly desirable. The process of generating such distinguishing patterns is termed as Diagnostic PatternGeneration. The goal of an automatic diagnostic patterngeneration (ADPG) is to generate a set of test patterns that is able to both detect all the detectable faults and make fully distinguishable all (detectable) faults that are not equivalent to each other. In general, we often prefer such a set of vectors to contain a small number of vectors. Most testgeneration systems are built around core ATPG algorithm for