efficient test sequence generation

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Efficient test sequence generation for Web applications with frames.

Efficient test sequence generation for Web applications with frames.

To evaluate our method, we compare our testing method with the one based on Aho’s algorithm for RPP. Given a strongly-connected digraph G from a specification FSM o f a web application, our method is to put all edges in G that are equivalent into a same subset and then make use o f the algorithm o f GPP to generate a reduced-length test sequence. On the other hand, the method o f using Aho’s algorithm will generate an optimal test sequence which can cover all required edges (do not contain the edges from ‘back hom e’ button) at least once. The reason we compare with A ho’s algorithm is that the assumptions subset R used in this algorithm hold in our setting: we assume we can reach all web pages from home page in web applications, and as a result, the subset R o f G from a specification FSM is a weekly connected spanning subgraph. Here we compare the lengths o f test sequences that are generated by the two different methods and the time to generate the test sequences. The FSM is input into our Java program as a directed strongly connected graph to generate a test sequence. The length and time can be affected by the number o f vertices, edges and subsets. In this section, we present our experimental result for the effect o f the three factors on the length and time by fixing the value o f the other two factors.
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An Efficient Approach to Automatic Generation of Time-lapse Video Sequence

An Efficient Approach to Automatic Generation of Time-lapse Video Sequence

eliminate the need for skilled video editors to spend excessive amount of time manually generating time-lapse videos. Furthermore, the automated analysis of the captured data will allow the system to intelligently select the most appropriate frames to be used for the sequence and enhance these selections to further improve the quality of the final output. This approach can be divided into three key stages: 1) efficient processing environment conversion; 2) frame analysis; 3) frame enhancement. The time-lapse video can then be rendered based on the optimal selection and enhancement of frames. Throughout the development of the system, data has been collected from various cameras located at numerous construction sites, which is subsequently used to test the robustness of the developed system using different viewpoints, backgrounds and scene compositions.
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Test Case Generation for UML Behavioral Diagram by Traversal Algorithm

Test Case Generation for UML Behavioral Diagram by Traversal Algorithm

Abstract: Software testing play crucial role in the software development as it consumes lot of time and resources. However testing process needs to be more efficiently done because overall software quality relies upon good testing approach. The present research focus on generation of test cases from UML diagrams. The combination graph is made by using activity and sequence diagrams. These diagrams proves to be more efficient as activity diagram gives the dynamic behavior of the model and sequence diagram is used to understand detailed functionality of the system. In this paper, a combined approach using Breadth first and depth first search is proposed which will generate expected test cases. The comparative study is done for test case generation using BFS and DFS algorithm and the result proves that the DFS traversal algorithm provides more accurate result for path coverage.
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Test Pattern Generation Using Lfsr With Reseeding Scheme for Bist Designs

Test Pattern Generation Using Lfsr With Reseeding Scheme for Bist Designs

In this paper we present LFSR reseeding scheme for BIST. A time -to –market efficient algorithm is introduced for selecting reseeding points in the test sequence. This algorithm targets complete fault coverage and minimization of the test length. Functional broadside tests that avoid over testing by ensuring that a circuit traverses only reachable states during the functional clock cycles of a test[1]. These consist of the input vectors and the corresponding responses. They check for proper operation of a verified design by testing the internal chip nodes. Functional tests cover a very high percentage of modeled faults in logic circuits and their generation is the main topic of this paper. Function test sequence is generated by
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IJCSMC, Vol. 3, Issue. 7, July 2014, pg.161 – 167 RESEARCH ARTICLE A Dynamic Programming Based Approach for Test Sequence Generation

IJCSMC, Vol. 3, Issue. 7, July 2014, pg.161 – 167 RESEARCH ARTICLE A Dynamic Programming Based Approach for Test Sequence Generation

Here figure 2 is showing the test case criticality vectors. These vectors include the Fault Based Analysis, Module Interaction Analysis and User Perspective Analysis. These vectors are used to decide or identify the test case criticality. After identifying the criticality vector for software system and the relative criticality value, the next work is to assign the priority to the test cases based on which the test sequence identification process is carried on. The final stage of work is to generate the effective test sequence based on which the software test cases will be applied over the software project. The optimality is here identified under the cost vector.
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Neural AMR: Sequence to Sequence Models for Parsing and Generation

Neural AMR: Sequence to Sequence Models for Parsing and Generation

Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their applica- tion to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively lim- ited amount of labeled data and the non- sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unla- beled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external seman- tic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence- based AMR models are robust against ordering variations of graph-to-sequence conversions.
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An Empirical Study on the Effectiveness of Automated Test Case Generation Techniques

An Empirical Study on the Effectiveness of Automated Test Case Generation Techniques

We identified the technique that is most effective in general. By the word ‘general’ we mean the technique that performs best in meeting the objectives presented previously i.e. the technique that achieves the highest test coverage and the technique that program complexities have little or no effect on. We allocated a score to each objective and the total score was given as three (3), one for each objective. The Combinatorial technique scored the highest value of 3 because it performed better in all the objectives than the Concolic technique. The Concolic technique scored 1 for attaining at least level of test coverage. Table 4 shows the summary of the evaluation and it includes; the overall result for each technique based on the three metrics used in the study and the score for each technique.
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Automatic Test Cases Generation for Statechart Specifications from Semantics to Algorithm

Automatic Test Cases Generation for Statechart Specifications from Semantics to Algorithm

Testing is an important activity for checking the conformance of system implementation to system specification by means of test experiments. Traditionally, test cases are manually developed based on some informal, natural language description of the system specification. Because the natural language specifications often lead to ambiguities and misinterpretations about the system's required properties, such a test cases generation process is laborious and error-prone. Then automation of test cases generation seems a logical solution. In order to automate test cases generation, using formal specification language to specify target system is unavoidable. Formal specifications are concerned with mathematical modeling of systems. Due to their mathematical underpinning formal specification languages allow to specify systems with more precision, more consistency and less ambiguity. Test cases can be efficiently, effectively and systematically derived from the formal specifications. There are many formal languages, e.g., CCS, CSP, ACP, LOTOS, SDL, Z, PROMELA and Statechart can be used to obtain
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Using UML for Automatic Test Generation

Using UML for Automatic Test Generation

There is an increasing need for effective testing of software for complex safety-critical applications, such as avionics, medical, and other control systems. These software systems usually have clear high level descriptions, sometimes in formal representations. Unfortunately, most system level testing techniques are only descr ibed informally. This paper is part of a project that is attempting to provide a solid foundation for generating tests from system level software specifications via new coverage criteria. Formal coverage criteria over testers ways to decide what test inputs to use during testing.
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Automatic model - based test case generation for uml diagrams using tree travelsal algorithm

Automatic model - based test case generation for uml diagrams using tree travelsal algorithm

The study was conducted in the area of test case generation with particular emphasis on UML diagrams driven by the problems arising from existing methods such as lack of integration with various UML diagrams and tools, inability to cover all model elements of UML diagrams, failure to generate comprehensive test cases based on adequate coverage criteria and lack of support tools for automatic generation of test cases. In this study, the problems of existing test case generation methods were addressed to ultimately provide mechanism of mapping generated test cases to the modelled requirements so as to verify the correctness of the SUT. Subsequently, the main research question of the study is:
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Object Oriented Test Case Generation Technique using Genetic Algorithms

Object Oriented Test Case Generation Technique using Genetic Algorithms

There are mainly three types of testing approaches namely Model based testing, Specification based testing and code based testing. In specification-based techniques test cases are derived directly from the specification or from some other kind of model of what the system should do. The principle of code based testing is to have each and every statement in the program executed at least once during the test. Code based testing attempts to test all reachable elements in the software under the cost and time constraints. The testing process begins by first identifying areas in the program not being exercised by the current set of test cases, follow by creating additional test cases to increase the coverage. Methods proposed for structural code based testing include statement coverage, decision coverage and condition coverage. Model-based testing is an approach that bases common testing tasks such as test case generation and test result evaluation of a model. A model of software is a depiction of its behavior where behavior can be described in terms of the input sequences accepted by the system, the set of actions, conditions, the flow of data through the application’s modules and routines. There are numerous such models, and each describes different aspects of software behavior. For example, control flow, data flow, and program dependency graphs express how the implementation behaves by representing its source code structure. Decision tables and state machines, on the other hand, are used to describe external so-called black box behavior. Examples of some software testing models are finite state machines, state charts, the unified modeling language (UML), Markov chains and grammars. This paper concentrates on model based approach. Activities in MBT are building a model, generating expected inputs and outputs, running the tests, comparing the actual outputs with expected outputs and stop testing after a criterion is reached
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Automated Test Generation

Automated Test Generation

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,  test  generation  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.    
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Test Suite Generation

Test Suite Generation

regular tree grammar macro tree transducer. derivation trees derived trees[r]

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Test Case Generation from UML Sequence Diagram for Aadhaar Card Number based ATM System

Test Case Generation from UML Sequence Diagram for Aadhaar Card Number based ATM System

Software testing is the primary and core activity for the development of a high quality software. In software testing, a program is executed with the intent to find errors and correct them [1]. Reliability of the system is fully dependent on the software testing. Software quality assurance activities are performed to review and verify the software but these activities are not sufficient to produce good quality software. Effective testing strategy reduces the development cost of the software. In software testing, a set of test cases are designed to execute the program and verify the output with expected output. Generation of test cases is very crucial activity. Generation of test cases mainly involve test case design and execution of designed test cases. With increasing in functionality of software caused more complexity in the development of the software. Object-oriented analysis and design strategy are used to reduce the software development cost and increase the reusability of the developed software. Object-oriented software needs different type of testing strategy as these types of software use the concept of class, object, inheritance, polymorphism, etc. UML is used for analysis and design of object-oriented software system. For testing the object-oriented software system, test cases may be designed from UML models. UML models are developed at the early stage of software development process. So, errors may be available during early stages of the development process. Since test case generation is a time consuming
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A test generation for pairwise testing

A test generation for pairwise testing

Another approach to generating a pairwise test set is to use orthogonal arrays. The original method of orthogonal arrays requires that all parameters have the same number of values and that each pair of values be covered the same number of times [8]. The rst requirement can be relaxed by adding don't care values for missing values. But the use of don't care values creates extra tests [9]. The second requirement is considered unnecessary for software testing and also creates extra tests for pairwise testing [1].

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International Journal of Computer Science and Mobile Computing

International Journal of Computer Science and Mobile Computing

Considering structural mechanics, also worked to propose an optimal design of a truss pylon respecting the stress constraints of the elements but it is not an easy task to solve considering the number and loading of the structure. Inverse analysis is an efficient solution to reach these aims. This technique becomes more and more popular thanks to the increase of the computing capabilities. Computing costs have decreased and allow to handle complex optimization problems through meta heuristic methods for example to identify the solution of the problem like the mechanical parameters of a behaviour model of a soil to define the best section of the beams composing a truss structure or to optimize wood-plastic composite mechanical properties designed for decking and taking into account the environmental impact during the life cycle of the product. [7]
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Morphological Inflection Generation Using Character Sequence to Sequence Learning

Morphological Inflection Generation Using Character Sequence to Sequence Learning

Durrett and DeNero (2013) published the Wik- tionary inflection dataset with training, development and test splits. The development and test sets con- tain 200 inflection tables each and the training sets consist of the remaining data. This dataset con- tains inflections for German, Finnish and Span- ish. This dataset was further augmented by (Nico- lai et al., 2015), by adding Dutch verbs extracted from CELEX lexical database (Baayen et al., 1995), French verbs from Verbsite, an online French con- jugation dictionary and Czech nouns and verbs from the Prague Dependnecy Treebank (Hajiˇc et al., 2001). As the dataset for Czech contains many in- complete tables, we do not use it for our experi- ments. These datasets come with pre-specified train- ing/dev/test splits, which we use. For each of these sets, the training data is restricted to 80% of the to- tal inflection tables, with 10% for development and 10% for testing. We list the size of these datasets in Table 3.
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Packages Automatic Test Generation

Packages Automatic Test Generation

Abstract-Networks are getting larger and more complex, yet administrators rely on rudimentary tools such as and to debug problems. We propose an automated and systematic approach for testing and debugging networks called ―Automatic Test Packet Generation‖ (ATPG). ATPG reads router configurations and generates a device-independent model. The model is used to generate a minimum set of test packets to (minimally) exercise every link in the network or (maximally) exercise every rule in the network. Test packets are sent periodically, and detected failures trigger a separate mechanism to localize the fault. ATPG can detect both functional (e.g., incorrect firewall rule) and performance problems (e.g., congested queue). ATPG complements but goes beyond earlier work in static checking (which cannot detect liveness or performance faults) or fault localization (which only localize faults given liveness results). We describe our prototype ATPG implementation and results on two real-world data sets: Stanford
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Automatic Test Packet Generation

Automatic Test Packet Generation

enterprise network and a nationwide ISP. The results are encouraging: Thanks to the structure of real world rulesets, the number of test packets needed is surprisingly small. For the Stanford network with over 757 000 rules and more than 100 VLANs, we only need 4000 packets to exercise all forwarding rules and ACLs. On Internet2, 35 000 packets suffice to exercise all IPv4 forwarding rules. Put another way, we can check every rule in every router on the Stanford backbone 10 times every second by sending test packets that consume less than 1% of network bandwidth. The link cover for Stanford is even smaller, around 50 packets, which allows proactive liveness testing every millisecond using 1% of network bandwidth Based on the network model, ATPG generates the minimal number of test packets so that every forwarding rule in the network is exercised and covered by at least one test packet When an error is detected, ATPG uses a fault localization algorithm to determine the failing rules or links. of the ATPG system. The system first collects all the forwarding state from the network this usually involves reading the FIBs, ACLs, and config files, as well as obtaining the topology. ATPG uses Header Space Analysis to compute reachability between all the test terminals The result is then used by the test packet selection algorithm to compute a minimal set of test packets that can test These packets will be sent periodically by the test terminals If an error is detected, the fault localization algorithm is invoked to narrow down the cause of the error described Networking is the word basically relating to computers and their connectivity. It is very often used in the world of computers and their use in different connections. The term networking implies the link between two or more computers and their devices, with the vital purpose of sharing the data stored in the computers, with each other. The networks between the computing devices are very common these days due to the launch of various hardware and computer software which aid in making the activity much more convenient to build and use.
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Literature Review of Test Case Generation Techniques for Object Oriented System

Literature Review of Test Case Generation Techniques for Object Oriented System

The process of generating test cases from design will help to discover problems early in the development process and thus it save time and resources during development of the system. However, it is very difficult to select test cases from UML models. In UML, the behavior of a use case can be represented by using interaction, activity and state machine diagrams. Sequence diagrams capture the exchange of messages between objects during execution of a use case. It focuses on the order in which the messages are sent. Activity diagrams, on the other hand, focus upon control flow as well as the activity-based relationships among objects. These are very useful for visualizing the way several objects collaborate to get a job done. Ranjita Kumari Swain, Vikas Panthi and Praful Kumar Behera, “Generation of test cases using Activity Diagram” have generated the test cases using activity diagrams [2]. In that approach, first an activity flow graph is derived from activity diagram. Then, all the required information is extracted from the activity flow graph (AFG). The activity flow graph (AFG) for the activity diagram is created by traversing the activity diagram from beginning to end, showing choices, conditions, concurrent executions, loop statements. From the graph different control flow sequence are identified by traversing the AFG by depth first traversal technique. Next, an algorithm is proposed to generate all activity paths. Finally, test cases are generated using activity path coverage criteria. Then Case study is being presented on Soft drink Vending Machine (SVM).
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