Systematic Software Testing

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Systematic Testing [ edit ] Systematic testing refers to a complete, conformance testing approach to software testing , in which the tested unit is shown to conform exhaustively to a specification, up to the testing assumptions. Nuseibeh, Boston: Springer, , Categories : Unit testing. Namespaces Article Talk. Views Read Edit View history.

Systematic Functional Testing Approach - Georgia Tech - Software Development Process

Languages Add links. These test cases are significant for the acceptance of software by the users.

Systematic Software Testing (Artech House Computer Library (Hardcover))

Your software testing process will be expedited if you already have use cases documented for your project. To apply use case based technique, follow these steps:. A decision table is also referred as a Cause-Effect table.

This technique is used for the functions which respond to a combination of inputs or events. For example, a Submit button on form will only be enabled if the user has entered all required fields. In order to use decision table testing and to make your software testing effective, the first task is to identify such functionalities where output depends on a combination of inputs.

Systematic Software Testing

If there is large input set of combinations, divide it in smaller subsets so that the decision table remains manageable. For each function, create a table and list down all possible combinations of inputs and respective outputs. This might also help in identifying any condition that is overlooked by the requirements analyst.

You may like to read more about decision tables. Automated Testing is becoming popular to make the testing process efficiently.

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It unloads the testing effort from human testers and assures that no test case has been missed. Nothing beats the human mind armed with the experience. Make your day more efficient by combining experience with the talent. If you have worked on any other similar project before, use the experience gained by that project into use.

Systematic Software Testing

Experience-based software testing technique can be used when the experienced resources share their knowledge with the new and relatively inexperienced testers. First, we need to identify the test scenarios intelligently to systematically test every feature of the application. Then, we can take help from use cases to depict the behavior of application when used by different actors of the system.

Decision Table Based Testing would be useful to test the scenarios where output is dependent on a set of inputs or events. You can expedite the testing process by automating the test cases using the ReqTest tool.

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In fact, companies like Google, Tesla, etc. Therefore, systematically testing DL-based software systems will be a significant step towards increasing safety and reliability of sensitive and safety-critical DL systems. This project will design, implement, and evaluate a novel software testing framework to assess the reliability of the Deep Learning applications and detect buggy behaviors during the application development and maintenance phase.


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In particular, the proposed framework will develop novel white-box testing strategies, realistic test-case generation techniques, and regression testing techniques to assess DL applications. A unique characteristic of the DL-based programming paradigm is that the end applications highly depend on the training data.


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  • Therefore, the research will build novel white-box testing strategies to evaluate both the model and the training data together as a whole system. In addition, this research will design and deploy techniques to generate new test cases that capture the real-world corner-case behavior where the DL applications may fail. The project will also investigate how any changes in data or model architecture can impact a pre-trained model in order to guide regression test case selection and prioritization process.

    This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.