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Numerical software is used in a wide variety of applications including safety-critical systems, which have stringent correctness requirements, and whose failures have catastrophic consequences that endanger human life. Numerical bugs are known to be particularly difficult to diagnose and fix, largely due to the use of approximate representations of numbers such as floating point. Understanding the...
While tremendously useful, automated techniques for tuning the precision of floating-point programs face important scalability challenges. We present Blame Analysis, a novel dynamic approach that speeds up precision tuning. Blame Analysis performs floating-point instructions using different levels of accuracy for their operands. The analysis determines the precision of all operands such that a given...
Compositional dynamic test generation can achieve significant scalability by memoizing symbolic execution sub-paths as test summaries. In this paper, we formulate the problem of statically validating symbolic test summaries against code changes. Summaries that can be proved still valid using a static analysis of a new program version do not need to be retested or recomputed dynamically. In the presence...
Software is rapidly increasing in size and complexity. Static analyses must be designed to scale well if they are to be usable with realistic applications, but prior efforts have often been limited by available memory. We propose a database-backed strategy for large program analysis based on graph algorithms, using a Semantic Web database to manage representations of the program under analysis. Our...
Given the variety of numerical errors that can occur, floating-point programs are difficult to write, test and debug. One common practice employed by developers without an advanced background in numerical analysis is using the highest available precision. While more robust, this can degrade program performance significantly. In this paper we present Precimonious, a dynamic program analysis tool to...
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