Results 1 - 10 of 7,427

Explaining Inconsistent Code

by Martin Schäf, Thomas Wies
"... A code fragment is inconsistent if it is not part of any normally terminating execution. Examples of such inconsistencies include code that is unreachable, code that always fails due to a run-time error, and code that makes conflicting assumptions about the program state. In this paper, we consider ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
the problem of automatically explaining inconsistent code. This problem is difficult because traditional fault localization techniques do not apply. Our solution relies on a novel algorithm that takes an infeasible code fragment as input and generates a so-called error invariant automaton. The error invariant

Explaining Inconsistent Code

by Muhammad Numair Mansur
"... ● 50 % of the time in debugging ● Fault localization. ● Becomes more tedious as the program size increase. ● Automatically explaining and localizing inconsistent code. ..."
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● 50 % of the time in debugging ● Fault localization. ● Becomes more tedious as the program size increase. ● Automatically explaining and localizing inconsistent code.

Extended Static Checking for Java

by Cormac Flanagan, K. Rustan M. Leino, Mark Lillibridge, Greg Nelson, James B. Saxe, Raymie Stata , 2002
"... Software development and maintenance are costly endeavors. The cost can be reduced if more software defects are detected earlier in the development cycle. This paper introduces the Extended Static Checker for Java (ESC/Java), an experimental compile-time program checker that finds common programming ..."
Abstract - Cited by 638 (24 self) - Add to MetaCart
of inconsistencies between the design decisions recorded in the annotations and the actual code, and also warns of potential runtime errors in the code. This paper gives an overview of the checker architecture and annotation language and describes our experience applying the checker to tens of thousands of lines

Ensemble Methods in Machine Learning

by Thomas G. Dietterich - MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857 , 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract - Cited by 625 (3 self) - Add to MetaCart
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging

KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs

by Cristian Cadar, Daniel Dunbar, Dawson Engler
"... We present a new symbolic execution tool, KLEE, capable of automatically generating tests that achieve high coverage on a diverse set of complex and environmentally-intensive programs. We used KLEE to thoroughly check all 89 stand-alone programs in the GNU COREUTILS utility suite, which form the cor ..."
Abstract - Cited by 557 (15 self) - Add to MetaCart
of the developers’ own hand-written test suite. When we did the same for 75 equivalent tools in the BUSYBOX embedded system suite, results were even better, including 100 % coverage on 31 of them. We also used KLEE as a bug finding tool, applying it to 452 applications (over 430K total lines of code), where

Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

by Jonathan S. Yedidia, William T. Freeman, Yair Weiss - IEEE Transactions on Information Theory , 2005
"... Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
Abstract - Cited by 585 (13 self) - Add to MetaCart
Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems

Compressive sampling

by Emmanuel J. Candès , 2006
"... Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired res ..."
Abstract - Cited by 1441 (15 self) - Add to MetaCart
mathematical insights underlying this new theory, and explain some of the interactions between compressive sampling and other fields such as statistics, information theory, coding theory, and theoretical computer science.

Synchronous data flow

by Edward A. Lee, et al. , 1987
"... Data flow is a natural paradigm for describing DSP applications for concurrent implementation on parallel hardware. Data flow programs for signal processing are directed graphs where each node represents a function and each arc represents a signal path. Synchronous data flow (SDF) is a special case ..."
Abstract - Cited by 622 (45 self) - Add to MetaCart
with data flow evaporates. Multiple sample rates within the same system are easily and naturally handled. Conditions for correctness of SDF graph are explained and scheduling algorithms are described for homogeneous parallel processors sharing memory. A preliminary SDF software system for automatically

Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems

by Steven Minton, Mark D. Johnston, Andrew B. Philips, Philip Laird - ARTIF. INTELL , 1992
"... This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value-orderin ..."
Abstract - Cited by 457 (6 self) - Add to MetaCart
This paper describes a simple heuristic approach to solving large-scale constraint satisfaction and scheduling problems. In this approach one starts with an inconsistent assignment for a set of variables and searches through the space of possible repairs. The search can be guided by a value

Golden Eggs and Hyperbolic Discounting

by David Laibson - Quarterly Journal of Economics , 1997
"... Hyperbolic discount functions induce dynamically inconsistent preferences, implying a motive for consumers to constrain their own future choices. This paper analyzes the decisions of a hyperbolic consumer who has access to an imperfect commitment technology: an illiquid asset whose sale must be init ..."
Abstract - Cited by 433 (14 self) - Add to MetaCart
Hyperbolic discount functions induce dynamically inconsistent preferences, implying a motive for consumers to constrain their own future choices. This paper analyzes the decisions of a hyperbolic consumer who has access to an imperfect commitment technology: an illiquid asset whose sale must
Results 1 - 10 of 7,427