Abstract: Methods and systems are provided for computer software testing using test suite data. A method may include defining a plurality of testing goals and a testing strategy for the code of the software application, determining objects under test within said code of a software application, designing test cases and test suites for said defined testing strategy, defining test categories for said designed test suites, defining a test execution sequence for said designed test suites and said test categories, defining whether a test execution sequence shall continue or stop after an error in a test object or a fail event in the test system, based on the results of the previous steps, parametrizing a test automation framework with the test suites, running the test automation framework on said code of a software application, and analyzing the results obtained from running the test automation framework on said code of a software application.

Abstract: In a method and apparatus of performing unit testing of a software module, the method provides for reading, by a computer, target data and discovering of functional aspects of a piece of software code, dividing the target data into chunks, estimating a plurality of decision/condition statements of the software code, estimating an amount of possible test cases based on the program inputs, defining a data set over the plurality of identified decisions/conditions, finding subset relationships between all the defined data sets, defining a plurality of optimal data sets, classifying the condition of the plurality of optimal data sets by category, refining the plurality of optimal data sets, and calculating the best amount of data sets.

Abstract: A method of estimating testing efforts for software unit testing, has at least the steps of assigning each element of a software system present in a development environment a complexity factor, calculating a complexity factor of the software system present in the development environment, and estimating an effort needed for performing testing of a unit under test in the development environment.

Abstract: Methods and systems are provided for computer software testing using test suite data. A method may include defining a plurality of testing goals and a testing strategy for the code of the software application, determining objects under test within said code of a software application, designing test cases and test suites for said defined testing strategy, defining test categories for said designed test suites, defining a test execution sequence for said designed test suites and said test categories, defining whether a test execution sequence shall continue or stop after an error in a test object or a fail event in the test system, based on the results of the previous steps, parametrizing a test automation framework with the test suites, running the test automation framework on said code of a software application, and analyzing the results obtained from running the test automation framework on said code of a software application.

Abstract: A method of estimating testing efforts for software unit testing, has at least the steps of assigning each element of a software system present in a development environment a complexity factor, calculating a complexity factor of the software system present in the development environment, and estimating an effort needed for performing testing of a unit under test in the development environment.

Abstract: In a method and apparatus of performing unit testing of a software module, the method provides for reading, by a computer, target data and discovering of functional aspects of a piece of software code, dividing the target data into chunks, estimating a plurality of decision/condition statements of the software code, estimating an amount of possible test cases based on the program inputs, defining a data set over the plurality of identified decisions/conditions, finding subset relationships between all the defined data sets, defining a plurality of optimal data sets, classifying the condition of the plurality of optimal data sets by category, refining the plurality of optimal data sets, and calculating the best amount of data sets.