Reverse‐engineering is the process of extracting system abstractions and design information out of existing software systems. This process involves the identification of software artefacts in a particular subject system, the exploration of how these artefacts interact with one another, and their aggregation to form more abstract system representations that facilitate program understanding. This paper describes our approach to creating higher‐level abstract representations of a subject system, which involves the identification of related components and dependencies, the construction of layered subsystem structures, and the computation of exact interfaces among subsystems. We show how top‐down decompositions of a subject system can be (re)constructed via bottom‐up subsystem composition. This process involves identifying groups of building blocks (e.g., variables, procedures, modules, and subsystems) using composition operations based on software engineering principles such as low coupling and high cohesion. The result is an architecture of layered subsystem structures. The structures are manipulated and recorded using the Rigi system, which consists of a distributed graph editor and a parsing system with a central repository. The editor provides graph filters and clustering operations to build and explore subsystem hierarchies interactively. The paper concludes with a detailed, step‐by‐step analysis of a 30‐module software system using Rigi.

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Abstract. Generation of efficient code for object-oriented programs requires knowledge of object lifetimes and method bindings. For object-oriented languages that have automatic storage management and dynamic look-up of methods, the compiler must obtain such knowledge by performing static analysis of the source code. We present an analysis algorithm which discovers the potential classes of each object in an object-oriented program as well as a safe approximation of their lifetimes. These results are obtained using abstract domains that approximate memory configurations and interprocedural call patterns of the program. We present several alternatives for these abstract domains that permit a trade-off between accuracy and complexity of the overall analysis.

Flow grammars provide a new mechanism for modeling control flow in flow analyzers and code optimizers. Existing methods for representing control flow are inadequate in terms of both their generality and their accuracy. Flow grammars overcome these deficiencies and are well-suited to the specification and solution of data flow analysis problems.

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