We describe an approach for automatically generating application aware graphical modeling environments from the meta-model specification of an application domain. A generated graphical modeling environment: a) provides domain-specific graphical metaphors in the modeling palette, b) imposes domain-specific modeling constraints to prevent semantically incorrect models, and c) provides domain-specific operators and languages to capture application domain constraints. The domain meta-model is specified using a meta-model which is an extension of the UML meta-model. One of the major advantage of using application domain-specific modeling environment is to make it easy for business analyst to create semantically correct models. We use UML2.0 to specify the domain metamodel. The advantage of using UML2.0 representation is the reuse of vendor-supported technologies including MDA tools. An implementation using the Eclipse framework is also discussed

The purpose of regression testing is to ensure that changes made to software, such as adding new features or modifying existing features, have not adversely affected features of the software that should not change. Regression testing is usually performed by running some, or all, of the test cases created to test modifications in previous versions of the software. Many techniques have been reported on how to select regression tests so that the number of test cases does not grow too large as the software evolves. Our proposed hybrid technique combines modification, minimization and prioritization-based selection using a list of source code changes and the execution traces from test cases run on previous versions. This technique seeks to identify a representative subset of all test cases that may result in different output behavior on the new software version. We report our experience with a tool called ATAC (Automatic Testing Analysis tool in C) which implements this technique.

Finding a fault in a program is a complex process which involves understanding the program's purpose, structure, semantics, and the relevant characteristics of failure producing tests. We describe a tool which supports execution slicing and dicing based on test cases. We report the results of an experiment that uses heuristic techniques in fault localization.

Program slices have potential uses in many software engineering applications. Traditional slicing algorithms, however, do not work correctly on programs that contain explicit jump statements. Two similar algorithms were proposed recently to alleviate this problem. Both require the flowgraph and the program dependence graph of the program to be modified. In this paper, we propose an alternative algorithm that leaves these graphs intact and uses a separate graph to store the additional required information. We also show that this algorithm permits an extremely efficient, conservative adaptation for use with programs that contain only “structured” jump statements.

In this paper we present techniques to find subsets of nodes of a flowgraph that satisfy the following property: A test set that exercises all nodes in a subset exercises all nodes in the flowgraph. Analogous techniques to find subsets of edges are also proposed. These techniques may be used to significantly reduce the cost of coverage testing of programs. A notion of a super block consisting of one or more basic blocks in that super block must be exercised by the same input. Dominator relationships among super blocks are used to identify a subset of the super blocks whose coverage implies that of all super blocks and, in turn, that of all basic blocks. Experiments with eight systems in the range of 1-75K lines of code show that, on the average, test cases targeted to cover just 29% of the basic blocks and 32% of the branches ensure 100% block and branch coverage, respectively.

The purpose of regression testing is to ensure that bug fixes and new functionality introduced in a new version of a software do not adversely affect the correct functionality inherited from the previous version. Efficient methods of selecting small subsets of regression test sets that can be used to ensure correct functionality are explored.>

Abstract Programmers spend considerable time debugging code. Symbolic debuggers provide some help but the task remains complex and difficult. Other than breakpoints and tracing, these tools provide little high‐level help. Programmers must perform many tasks manually that the tools could perform automatically, such as finding which statements in the program affect the value of an output variable for a given test case, and what was the value of a given variable when the control last reached a given program location. If debugging tools provided explicit support for these tasks, the debugging process could be automated to a significant extent. In this paper we present a debugging model, based on dynamic program slicing and execution backtracking techniques, that easily lends itself to automation. This model is based on experience with using these techniques to debug software. We also present a prototype debugging tool, SPYDER, that explicitly supports the proposed model, and with which we are performing further debugging research.

Program slices are useful in debugging, testing, maintenance, and understanding of programs. The conventional notion of a program slice, the static slice, is the set of all statements that might affect the value of a given variable occurrence. In this paper, we investigate the concept of the dynamic slice consisting of all statements that actually affect the value of a variable occurrence for a given program input. The sensitivity of dynamic slicing to particular program inputs makes it more useful in program debugging and testing than static slicing. Several approaches for computing dynamic slices are examined. The notion of a Dynamic Dependence Graph and its use in computing dynamic slices is discussed. The Dynamic Dependence Graph may be unbounded in length; therefore, we introduce the economical concept of a Reduced Dynamic Dependence Graph, which is proportional in size to the number of dynamic slices arising during the program execution.