94,713 research outputs found
Automatic Software Repair: a Bibliography
This article presents a survey on automatic software repair. Automatic
software repair consists of automatically finding a solution to software bugs
without human intervention. This article considers all kinds of repairs. First,
it discusses behavioral repair where test suites, contracts, models, and
crashing inputs are taken as oracle. Second, it discusses state repair, also
known as runtime repair or runtime recovery, with techniques such as checkpoint
and restart, reconfiguration, and invariant restoration. The uniqueness of this
article is that it spans the research communities that contribute to this body
of knowledge: software engineering, dependability, operating systems,
programming languages, and security. It provides a novel and structured
overview of the diversity of bug oracles and repair operators used in the
literature
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Automatic Generation of Cognitive Theories using Genetic Programming
Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming. Our approach evolves from experimental data cognitive theories that explain âthe mental programâ that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories
From a Domain Analysis to the Specification and Detection of Code and Design Smells
Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud
on development and maintenance. Consequently, several smell detection\ud
approaches and tools have been proposed in the literature. However,\ud
so far, they allow the detection of predefined smells but the detection\ud
of new smells or smells adapted to the context of the analysed systems\ud
is possible only by implementing new detection algorithms manually.\ud
Moreover, previous approaches do not explain the transition from\ud
specifications of smells to their detection. Finally, the validation\ud
of the existing approaches and tools has been limited on few proprietary\ud
systems and on a reduced number of smells. In this paper, we introduce\ud
an approach to automate the generation of detection algorithms from\ud
specifications written using a domain-specific language. This language\ud
is defined from a thorough domain analysis. It allows the specification\ud
of smells using high-level domain-related abstractions. It allows\ud
the adaptation of the specifications of smells to the context of\ud
the analysed systems.We specify 10 smells, generate automatically\ud
their detection algorithms using templates, and validate the algorithms\ud
in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud
v1.10.2, two open-source object-oriented systems.We also compare\ud
the detection results with those of a previous approach, iPlasma
Exploiting Query Structure and Document Structure to Improve Document Retrieval Effectiveness
In this paper we present a systematic analysis of document
retrieval using unstructured and structured queries within
the score region algebra (SRA) structured retrieval framework. The behavior of diÂźerent retrieval models, namely
Boolean, tf.idf, GPX, language models, and Okapi, is tested
using the transparent SRA framework in our three-level structured retrieval system called TIJAH. The retrieval models are implemented along four elementary retrieval aspects: element and term selection, element score computation, score combination, and score propagation.
The analysis is performed on a numerous experiments
evaluated on TREC and CLEF collections, using manually
generated unstructured and structured queries. Unstructured queries range from the short title queries to long title
+ description + narrative queries. For generating structured
queries we exploit the knowledge of the document structure
and the content used to semantically describe or classify
documents. We show that such structured information can
be utilized in retrieval engines to give more precise answers to user queries then when using unstructured queries
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BIF : a behavioral intermediate format for high level synthesis
This report describes a new intermediate format for behavioral synthesis systems, based on annotated state tables. It supports user control of the synthesis process by allowing specification of partial design structures, user-bindings and user modification of compiled designs. It is a simple and uniform representation that can be used as an intermediate exchange format for various behavioral synthesis tools. The format captures synchronous and asynchronous behavior, and serves as a good interface to the user by linking behavior and structure at each level of abstraction in the behavioral synthesis process
Integration of finite element modeling with solid modeling through a dynamic interface
Finite element modeling is dominated by geometric modeling type operations. Therefore, an effective interface to geometric modeling requires access to both the model and the modeling functionality used to create it. The use of a dynamic interface that addresses these needs through the use of boundary data structures and geometric operators is discussed
A Domain Analysis to Specify Design Defects and Generate Detection Algorithms
Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud
and maintenance. Consequently, several defect detection approaches\ud
and tools have been proposed in the literature. However, we are not\ud
aware of any approach that defines and reifies the process of generating\ud
detection algorithms from the existing textual descriptions of defects.\ud
In this paper, we introduce an approach to automate the generation\ud
of detection algorithms from specifications written using a domain-specific\ud
language. The domain-specific is defined from a thorough domain analysis.\ud
We specify several design defects, generate automatically detection\ud
algorithms using templates, and validate the generated detection\ud
algorithms in terms of precision and recall on Xerces v2.7.0, an\ud
open-source object-oriented system
The Automatic Inference of State Invariants in TIM
As planning is applied to larger and richer domains the effort involved in
constructing domain descriptions increases and becomes a significant burden on
the human application designer. If general planners are to be applied
successfully to large and complex domains it is necessary to provide the domain
designer with some assistance in building correctly encoded domains. One way of
doing this is to provide domain-independent techniques for extracting, from a
domain description, knowledge that is implicit in that description and that can
assist domain designers in debugging domain descriptions. This knowledge can
also be exploited to improve the performance of planners: several researchers
have explored the potential of state invariants in speeding up the performance
of domain-independent planners. In this paper we describe a process by which
state invariants can be extracted from the automatically inferred type
structure of a domain. These techniques are being developed for exploitation by
STAN, a Graphplan based planner that employs state analysis techniques to
enhance its performance
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