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The Testing Techniques for Parallel Software (TTPS) project was conducted by Optimization Technology, Inc. for Rome Laboratory. The purpose was to investigate and implement candidate parallel software testing techniques to improve the quality and reliability of parallel software through the application of automated software testing tools and techniques. Although software testing tools are beginning to mature for the sequential software development environment, the availability of software quality and test tools for the parallel environment continues to be poor. The TTPS project focused on the incorporation of candidate techniques into an operational demonstration tool. The TTPS tool delivers parallel software testing techniques, automated documentation, and metrics integrated into a high quality software quality tool ready for real world testing application.

The Testing Techniques for Parallel Software (TTPS) project was conducted by Optimization Technology, Inc. for Rome Laboratory. The purpose was to investigate and implement candidate parallel software testing techniques to improve the quality and reliability of parallel software through the application of automated software testing tools and techniques. Although software testing tools are beginning to mature for the sequential software development environment, the availability of software quality and test tools for the parallel environment continues to be poor. The TTPS project focused on the incorporation of candidate techniques into an operational demonstration tool. The TTPS tool delivers parallel software testing techniques, automated documentation, and metrics integrated into a high quality software quality tool ready for real world testing application.

e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer science courses, programming exercises are widely used and courses usually have a very large number of participants. However, there is still no efficient way for supporting tutors to correct these exercises, as experience has shown that correction (and, beyond that, automatic grading) are difficult and time consuming. In this paper we present an enhancement of the xLx platform developed at the University of Muenster to efficiently support tutors in handling Java programming exercises electronically. The new component is based on concepts of automatic static and dynamic testing approaches, well known from software engineering, and provides an automatic pre-correction of submitted solutions. In addition, a tutor is able to annotate solutions manually, by adding comments that are associated with the source code of the solution in an intelligent way. Static tests are based on a compilation of the sources to find syntactical errors, while dynamic tests use test cases defined by tutors during the creation of the exercises and have to be executed correctly on the solutions in order to receive credits for the exercises.

e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer science courses, programming exercises are widely used and courses usually have a very large number of participants. However, there is still no efficient way for supporting tutors to correct these exercises, as experience has shown that correction (and, beyond that, automatic grading) are difficult and time consuming. In this paper we present an enhancement of the xLx platform developed at the University of Muenster to efficiently support tutors in handling Java programming exercises electronically. The new component is based on concepts of automatic static and dynamic testing approaches, well known from software engineering, and provides an automatic pre-correction of submitted solutions. In addition, a tutor is able to annotate solutions manually, by adding comments that are associated with the source code of the solution in an intelligent way. Static tests are based on a compilation of the sources to find syntactical errors, while dynamic tests use test cases defined by tutors during the creation of the exercises and have to be executed correctly on the solutions in order to receive credits for the exercises.

e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer science courses, programming exercises are widely used and courses usually have a very large number of participants. However, there is still no efficient way for supporting tutors to correct these exercises, as experience has shown that correction (and, beyond that, automatic grading) are difficult and time consuming. In this paper we present an enhancement of the xLx platform developed at the University of Muenster to efficiently support tutors in handling Java programming exercises electronically. The new component is based on concepts of automatic static and dynamic testing approaches, well known from software engineering, and provides an automatic pre-correction of submitted solutions. In addition, a tutor is able to annotate solutions manually, by adding comments that are associated with the source code of the solution in an intelligent way. Static tests are based on a compilation of the sources to find syntactical errors, while dynamic tests use test cases defined by tutors during the creation of the exercises and have to be executed correctly on the solutions in order to receive credits for the exercises.

e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer science courses, programming exercises are widely used and courses usually have a very large number of participants. However, there is still no efficient way for supporting tutors to correct these exercises, as experience has shown that correction (and, beyond that, automatic grading) are difficult and time consuming. In this paper we present an enhancement of the xLx platform developed at the University of Muenster to efficiently support tutors in handling Java programming exercises electronically. The new component is based on concepts of automatic static and dynamic testing approaches, well known from software engineering, and provides an automatic pre-correction of submitted solutions. In addition, a tutor is able to annotate solutions manually, by adding comments that are associated with the source code of the solution in an intelligent way. Static tests are based on a compilation of the sources to find syntactical errors, while dynamic tests use test cases defined by tutors during the creation of the exercises and have to be executed correctly on the solutions in order to receive credits for the exercises.

An experiment was performed in which executable assertions were used in conjunction with search techniques in order to test a computer program automatically. The program chosen for the experiment computes a position on an orbit from the description of the orbit and the desired point. Errors were inserted in the program randomly using an error generation method based on published data defining common error types. Assertions were written for the program and it was tested using two different techniques. The first divided up the range of the input variables and selected test cases from within the subranges. In this way a grid of test values was constructed over the program's input space. The second used a search algorithm from optimization theory. This entailed using the assertions to define an error function and then maximizing its value. The program was then tested by varying only a limited number of the input variables and a second time by varying all of them. The results indicate that this search testing technique was as effective as the grid testing technique in locating errors and was more efficient. In addition, the search testing technique located critical input values which helped in writing correct assertions. (Author)

An experiment was performed in which executable assertions were used in conjunction with search techniques in order to test a computer program automatically. The program chosen for the experiment computes a position on an orbit from the description of the orbit and the desired point. Errors were inserted in the program randomly using an error generation method based on published data defining common error types. Assertions were written for the program and it was tested using two different techniques. The first divided up the range of the input variables and selected test cases from within the subranges. In this way a grid of test values was constructed over the program's input space. The second used a search algorithm from optimization theory. This entailed using the assertions to define an error function and then maximizing its value. The program was then tested by varying only a limited number of the input variables and a second time by varying all of them. The results indicate that this search testing technique was as effective as the grid testing technique in locating errors and was more efficient. In addition, the search testing technique located critical input values which helped in writing correct assertions. (Author)

Bibliography: p. 475-496

Bibliography: p. 475-496