Improved Software Testing for Open Architecture

Proceedings Paper (for Acquisition Research Program)Applying traditional manual US Navy testing practices to OA systems will limit many benefits of OA, such as system scalability, rapid configuration changes, and effective component reuse. Pairing profile-driven automated software testing with test reduction techniques should enable these benefits and keep resource requirements at feasible levels. Test cases generated by operational profiles have been shown to be more effective than those developed by other methods, such as random or selective testing, and more resource-efficient than exhaustive approaches. This research effort increases the fidelity of the operational profile, creating an environment model referred to as a High-Fidelity Profile Model (HFPM) that can statistically describe individual software inputs. Samples from the HFPM''s probability distributions can generate operationally realistic or overly-stressful test cases for software modules under test. This process can be automated and paired with output checking functions, enabling automated effective software testing, and potentially improving reliability. Such models would be ideal for US Navy Open Architecture (OA) software because of the defined interface standards. HFPMs can enable effective testing in software reuse applications and are ideal for testing multiple releases of maturing software. This research defines the HFPM, presents a methodology to develop, validate, and apply it.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions

Estimation of probability density functions (pdf) is one major topic in pattern recognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. Artificial neural networks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf

Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions

Abstract. Estimation of probability density functions (pdf) is one major topic in pattern recognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. Artificial neural networks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.