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Results for “deductive code synthesis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Deductive Glue Code Synthesis for Embedded Software Systems Based on Code Patterns

Automated code synthesis is a constructive process that can be used to generate programs from specifications. It can, thus, greatly reduce the software development cost and time. The use of formal code synthesis approach for software generation further increases the dependability of the system. Though code synthesis has many potential benefits, the synthesis techniques are still limited. Meanwhile, components are widely used in embedded system development. Applying code synthesis to component based software development (CBSD) process can greatly enhance the capability of code synthesis while reducing the component composition efforts. In this paper, we discuss the issues and techniques for applying deductive code synthesis techniques to CBSD. For deductive synthesis in CBSD, a rule base is the key for inferring appropriate component composition. We use the code patterns to guide the development of rules. Code patterns have been proposed to capture the typical usages of the components. Several general composition operations have been identified to facilitate systematic composition. We present the technique for rule development and automated generation of new patterns from existing code patterns. A case study of using this method in building a real-time control system is also presented.

automated code synthesis↗
NASA NTRS1998

Explaining Synthesized Software

Motivated by NASA's need for high-assurance software, NASA Ames' Amphion project has developed a generic program generation system based on deductive synthesis. Amphion has a number of advantages, such as the ability to develop a new synthesis system simply by writing a declarative domain theory. However, as a practical matter, the validation of the domain theory for such a system is problematic because the link between generated programs and the domain theory is complex. As a result, when generated programs do not behave as expected, it is difficult to isolate the cause, whether it be an incorrect problem specification or an error in the domain theory. This paper describes a tool we are developing that provides formal traceability between specifications and generated code for deductive synthesis systems. It is based on extensive instrumentation of the refutation-based theorem prover used to synthesize programs. It takes augmented proof structures and abstracts them to provide explanations of the relation between a specification, a domain theory, and synthesized code. In generating these explanations, the tool exploits the structure of Amphion domain theories, so the end user is not confronted with the intricacies of raw proof traces. This tool is crucial for the validation of domain theories as well as being important in everyday use of the code synthesis system. It plays an important role in validation because when generated programs exhibit incorrect behavior, it provides the links that can be traced to identify errors in specifications or domain theory. It plays an important role in the everyday use of the synthesis system by explaining to users what parts of a specification or of the domain theory contribute to what pieces of a generated program. Comments are inserted into the synthesized code that document these explanations.

VanBaalen, Jeffrey↗
NASA NTRS2001

The AutoBayes Program Synthesis System: System Description

AUTOBAYES is a fully automatic program synthesis system for the statistical data analysis domain. Its input is a concise description of a data analysis problem in the form of a statistical model; its output is optimized and fully documented C/C++ code which can be linked dynamically into the Matlab and Octave environments. AUTOBAYES synthesizes code by a schema-guided deductive process. Schemas (i.e., code templates with associated semantic constraints) are applied to the original problem and recursively to emerging subproblems. AUTOBAYES complements this approach by symbolic computation to derive closed-form solutions whenever possible. In this paper, we concentrate on the interaction between the symbolic computations and the deductive synthesis process. A statistical model specifies for each problem variable (i.e., data or parameter) its properties and dependencies in the form of a probability distribution, A typical data analysis task is to estimate the best possible parameter values from the given observations or measurements. The following example models normal-distributed data but takes prior information (e.g., from previous experiments) on the data's mean value and variance into account.

Fischer, Bernd↗
NASA NTRS2009

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Denney, Ewen↗
NASA NTRS1999

Transformation Systems at NASA Ames

In this paper, we describe the experiences of the Automated Software Engineering Group at the NASA Ames Research Center in the development and application of three different transformation systems. The systems span the entire technology range, from deductive synthesis, to logic-based transformation, to almost compiler-like source-to-source transformation. These systems also span a range of NASA applications, including solving solar system geometry problems, generating data analysis software, and analyzing multi-threaded Java code.

Buntine, Wray↗