This paper introduces DIDUCE, a practical and effective tool that aids programmers in detecting complex program errors and identifying their root causes. By instrumenting a program and observing its behavior as it runs, DIDUCE dynamically formulates hypotheses of invariants obeyed by the program. DIDUCE hypothesizes the strictest invariants at the beginning, and gradually relaxes the hypothesis as violations are detected to allow for new behavior. The violations reported help users to catch software bugs as soon as they occur. They also give programmers new visibility into the behavior of the programs such as identifying rare corner cases in the program logic or even locating hidden errors that corrupt the program's results.

Understanding the implementation of a certain feature of a system requires to identify the computational units of the system that contribute to this feature. In many cases, the mapping of features to the source code is poorly documented. In this paper, we present a semi-automatic technique that reconstructs the mapping for features that are triggered by the user and exhibit an observable behavior. The mapping is in general not injective; that is, a computational unit may contribute to several features. Our technique allows to distinguish between general and specific computational units with respect to a given set of features. For a set of features, it also identifies jointly and distinctly required computational units.

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Testing is an important process that is performed to support quality assurance. Testing activities support quality assurance by gathering information about the nature of the software being studied. These activities consist of designing test cases, executing the software with those test cases, and examining the results produced by those executions. Studies indicate that more than fifty percent of the cost of software development is devoted to testing, with the percentage for testing critical software being even higher. As software becomes more pervasive and is used more often to perform critical tasks, it will be required to be of higher quality. Unless we can find efficient ways to perform effective testing, the percentage of development costs devoted to testing will increase significantly. This report briefly assesses the state of the art in software testing, outlines some future directions in software testing, and gives some pointers to software testing resources.

Producing specifications by dynamic (runtime) analysis of program executions is potentially unsound, because the analyzed executions may not fully characterize all possible executions of the program. In practice, how accurate are the results of a dynamic analysis? This paper describes the results of an investigation into this question, determining how much specifications generalized from program runs must be changed in order to be verified by a static checker.

This paper shows how to integrate two complementary techniques for manipulating program invariants: dynamic detection and static verification. Dynamic detection proposes likely invariants based on program executions, but the resulting properties are not guaranteed to be true over all possible executions. Static verification checks that properties are always true, but it can be difficult and tedious to select a goal and to annotate programs for input to a static checker. Combining these techniques overcomes the weaknesses of each: dynamically detected invariants can annotate a program or provide goals for static verification, and static veri cation can confirm properties proposed by a dynamic tool. We have

Modeling object-oriented applications using collaborations and roles is now well accepted. Collaboration-based or role-based designs decompose an application into tasks performed by a subset of the applications' classes. Collaborations provide a larger unit of understanding and reuse than classes, and are an important aid in the maintenance and evolution of the software. This kind of design information is lost, however, at the implementation level, making it hard to maintain and evolve an existing software application. The extraction of collaborations from code is therefore an important issue in design recovery. In this paper we propose an iterative approach which uses dynamic information to support the recovery and understanding of collaborations. We describe a tool we have developed to support our approach and demonstrate its use on a case study.

A new method is presented for analyzing and reengineering class hierarchies. In our approach, a class hierarchy is processed along with a set of applications that use it, and a fine-grained analysis of the access and subtype relationships between objects, variables, and class members is performed. The result of this analysis is again a class hierarchy, which is guaranteed to be behaviorally equivalent to the original hierarchy, but in which each object only contains the members that are required. Our method is semantically well-founded in concept analysis: the new class hierarchy is a minimal and maximally factorized concept lattice that reflects the access and subtype relationships between variables, objects and class members. The method is primarily intended as a tool for finding imperfections in the design of class hierarchies, and can be used as the basis for tools that largely automate the process of reengineering such hierarchies. The method can also be used as a space-optimizing source-to-source transformation that removes redundant fields from objects. A prototype implementation for Java has been constructed, and used to conduct several case studies. Our results demonstrate that the method can provide valuable insights into the usage of a class hierarchy in a specific context, and lead to useful restructuring proposals.

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Abstract. Formal Concept Analysis (FCA) has typically been applied in the field of software engineering to support software maintenance and object-oriented class identification tasks. This paper presents a broader overview by describing and classifying academic papers that report the application of FCA to software engineering. The papers are classified using a framework based on the activities defined in the ISO12207 Software Engineering standard. Two alternate classification schemes based on the programming language under analysis and target application size are also discussed. In addition, the authors work to support agile methods and formal specification via FCA is introduced. 1