Monitoring software development through dynamic variables

Research conducted by the Software Engineering Laboratory (SEL) on the use of dynamic variables as a tool to monitor software development is described. Project independent measures which may be used in a management tool for monitoring software development are identified. Several FORTRAN projects with similar profiles are examined. The staff was experienced in developing these types of projects. The projects developed serve similar functions. Because these projects are similar some underlying relationships exist that are invariant between projects. These relationships, once well defined, may be used to compare the development of different projects to determine whether they are evolving the same way previous projects in this environment evolved

Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

Reachability for dynamic parametric processes

In a dynamic parametric process every subprocess may spawn arbitrarily many, identical child processes, that may communicate either over global variables, or over local variables that are shared with their parent. We show that reachability for dynamic parametric processes is decidable under mild assumptions. These assumptions are e.g. met if individual processes are realized by pushdown systems, or even higher-order pushdown systems. We also provide algorithms for subclasses of pushdown dynamic parametric processes, with complexity ranging between NP and DEXPTIME.Comment: 31 page

Nonparametric identification of dynamic models with unobserved state variables

We consider the identification of a Markov process {W t, X t*} for t=1,2,...,T when only {W t} for t=1, 2,..,T is observed. In structural dynamic models, W t denotes the sequence of choice variables and observed state variables of an optimizing agent, while X t* denotes the sequence of serially correlated state variables. The Markov setting allows the distribution of the unobserved state variable X t* to depend on W t-1 and X t-1 *. We show that the joint distribution of (W t, X t*, W t-1 , X t-1 *) is identified from the observed distribution of (W t+1 , W t, W t-1 , W t-2 , W t-3 ) under reasonable assumptions. Identification of the joint distribution of (W t, X t*, W t-1 , X t-1 *) is a crucial input in methodologies for estimating dynamic models based on the "conditional-choice-probability (CCP)" approach pioneered by Hotz and Miller.

Portuguese tourism demand:a dynamic panel data analysis

This article considers the determinants of Portuguese tourism demand for the period 2004-2013. The econometric methodology uses a panel unit root test and the dynamic panel data (GMM-system estimator). The different techniques of panel unit root (Levin, Lin and Chu; Im, Pesaran and Shin W-stat and augmented Dickey-Fuller - Fisher Chi-square) show that the variables used in this panel are stationary. The dynamic model proves that tourism demand is a dynamic process. The variables relative prices, income per capita, human capital and government spending encourage international tourism demand for Portugal.info:eu-repo/semantics/publishedVersio

A Semiparametric Estimator for Dynamic Optimization Models

We develop a new estimation methodology for dynamic optimization models with unobserved state variables Our approach is semiparametric in the sense of not requiring explicit parametric assumptions to be made concerning the distribution of these unobserved state variables We propose a two-step pairwise-difference estimator which exploits two common features of dynamic optimization problems: (1) the weak monotonicity of the agent's decision (policy) function in the unobserved state variables conditional on the observed state variables; and (2) the state-contingent nature of optimal decision-making which implies that conditional on the observed state variables the variation in observed choices across agents must be due to randomness in the unobserved state variables across agents We apply our estimator to a model of dynamic competitive equilibrium in the market for milk production quota in Ontario Canada