Bogdan Korel

Automated software test data generation

An alternative approach to test-data generation based on actual execution of the program under test, function-minimization methods and dynamic data-flow analysis is presented. Test data are developed for the program using actual values of input variables. When the program is executed, the program execution flow is monitored. If during program execution an undesirable execution flow is observed then function-minimization search algorithms are used to automatically locate the values of input variables for which the selected path is traversed. In addition, dynamic data-flow analysis is used to determine those input variables responsible for the undesirable program behavior, significantly increasing the speed of the search process. The approach to generating test data is then extended to programs with dynamic data structures and a search method based on dynamic data-flow analysis and backtracking is presented. In the approach described, values of array indexes and pointers are known at each step of program execution; this information is used to overcome difficulties of array and pointer handling. >

Dynamic program slicing

Abstract A dynamic program slice is an executable subset of the original program that produces the same computations on a subset of selected variables and inputs. It differs from the static slice (Weiser, 1982, 1984) in that it is entirely defined on the basis of a computation. The two main advantages are the following: Arrays and dynamic data structures can be handled more precisely and the size of slice can be significantly reduced, leading to a finer localization of the fault. The approach is being investigated as a possible extension of the debugging capabilities of STAD, a recently developed System for Testing and Debugging (Korel and Laski, 1987; Laski, 1987).

The chaining approach for software test data generation

Software testing is very labor intensive and expensive and accounts for a significant portion of software system development cost. If the testing process could be automated, the cost of developing software could be significantly reduced. Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as statement coverage and branch coverage. In this article we present a chaining approach for automated software test data generation which builds on the current theory of execution-oriented test data generation. In the chaining approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements. The existing methods of test data generation may not efficiently generate test data for these types of programs because they only use control flow information of a program during the search process. The chaining approach uses data dependence analysis to guide the search process, i.e., data dependence analysis automatically identifies statements that affect the execution of the selected statement. The chaining approach uses these statements to form a sequence of statements that is to be executed prior to the execution of the selected statement. The experiments have shown that the chaining approach may significantly improve the chances of finding test data as compared to the existing methods of automated test data generation.

A Data Flow Oriented Program Testing Strategy

Some properties of a program data flow can be used to guide program testing. The presented approach aims to exercise use-definition chains that appear in the program. Two such data oriented testing strategies are proposed; the first involves checking liveness of every definition of a variable at the point(s) of its possible use; the second deals with liveness of vectors of variables treated as arguments to an instruction or program block. Reliability of these strategies is discussed with respect to a program containing an error.

Dynamic slicing of computer programs

Abstract Program slicing is a useful tool in program debugging [25, 26]. Dynamic slicing introduced in this paper differs from the original static slicing in that it is defined on the basis of a computation. A dynamic program slice is an executable part of the original program that preserves part of the programs behavior for a specific input with respect to a subset of selected variables, rather than for all possible computations. As a result, the size of a slice can be significantly reduced. Moreover, the approach allows us to treat array elements and fields in dynamic records as individual variables. This leads to a further reduction of the slice size.

Model based regression test reduction using dependence analysis

Model based testing is a system testing technique used to test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective test generation techniques are used to test the modified parts of the model. However, the size of regression test suites still may be very large. In this paper, we present a model-based regression testing approach that uses EFSM model dependence analysis to reduce regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, regression test reduction strategies are used to reduce the regression test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Automated test data generation for programs with procedures

Test data generation in program testing is the process of identifying a set of test data that satisfies a selected testing criterion, such as, statement coverage or branch coverage. The existing methods of test data generation are limited to unit testing and may not efficiently generate test data for programs with procedures. In this paper we present an approach for automated test data generation for programs with procedures. This approach builds on the current theory of execution-oriented test data generation. In this approach, test data are derived based on the actual execution of the program under test. For many programs, the execution of the selected statement may require prior execution of some other statements that may be part of some procedures. The existing methods use only control flow information of a program during the search process and may not efficiently generate test data for these types of programs because they are not able to identify statements that affect execution of the selected statement. Our approach uses data dependence analysis to guide the process of test data generation. Data dependence analysis automatically identifies statements (or procedures) that affect the execution of the selected statement and this information is used to guide the search process. The initial experiments have shown that this approach may improve the chances of finding test data.

Evolutionary testing in the presence of loop-assigned flags: a testability transformation approach

Evolutionary testing is an effective technique for automatically generating good quality test data. However, for structural testing, the technique degenerates to random testing in the presence of flag variables, which also present problems for other automated test data generation techniques. Previous work on the flag problem does not address flags assigned in loops.This paper introduces a testability transformation that transforms programs with loop--assigned flags so that existing genetic approaches can be successfully applied. It then presents empirical data demonstrating the effectiveness of the transformation. Untransformed, the genetic algorithm flounders and is unable to find a solution. Two transformations are considered. The first allows the search to find a solution. The second reduces the time taken by an order of magnitude and, more importantly, reduces the slope of the cost increase; thus, greatly increasing the complexity of the problem to which the genetic algorithm can be applied. The paper also presents a second empirical study showing that loop--assigned flags are prevalent in real world code. They account for just under 11% of all flags.

Slicing of state-based models

System modeling is a widely used technique to model state-based systems. Several state-based languages are used to model such systems, e.g., EFSM (extended finite state machine), SDL (specification description language) and state charts. Although state-based modeling is very useful, system models are frequently large and complex and are hard to understand and modify. Slicing is a well-known reduction technique. Most of the research on slicing is code-based. There has been limited research on specification-based slicing and model-based slicing. In this paper, we present an approach to slicing state-based models, in particular EFSM models. Our approach automatically identifies the parts of the model that affect an element of interest using EFSM dependence analysis. Slice reduction techniques are then used to reduce the size of the EFSM slice. Our experience with the presented slicing approach showed that significant reduction of state-based models could be achieved.

Requirement-based automated black-box test generation

Testing large software systems is very laborious and expensive. Model-based test generation techniques are used to automatically generate tests for large software systems. However, these techniques require manually created system models that are used for test generation. In addition, generated test cases are not associated with individual requirements. In this paper, we present a novel approach of requirement-based test generation. The approach accepts a software specification as a set of individual requirements expressed in textual and SDL formats (a common practice in the industry). From these requirements, system model is automatically created with requirement information mapped to the model. The system model is used to automatically generate test cases related to individual requirements. Several test generation strategies are presented. The approach is extended to requirement-based regression test generation related to changes on the requirement level. Our initial experience shows that this approach may provide significant benefits in terms of reduction in number of test cases and increase in quality of a test suite.