An Empirical Study on the Procedure to Derive Software Quality Estimation Models

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value

Data assimilation of in situ soil moisture measurements in hydrological models: first annual doctoral progress report, work plan and achievements

Water scarcity and the presence of water of good quality is a serious public concern since it determines the availability of water to society. Water scarcity especially in arid climates and due to extreme droughts related to climate change drive water use technologies such as irrigation to become more efficient and sustainable. Plant root water and nutrient uptake is one of the most important processes in subsurface unsaturated flow and transport modeling, as root uptake controls actual plant evapotranspiration, water recharge and nutrient leaching to the groundwater, and exerts a major influence on predictions of global climate models. To improve irrigation strategies, water flow needs to be accurately described using advanced monitoring and modeling. Our study focuses on the assimilation of hydrological data in hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. Over the past year, we demonstrated the calibration and optimization of the Hydrus 1D model for an irrigated grassland on sandy soil. Direct and inverse calibration and optimization for both heterogeneous and homogeneous conceptualizations was applied. Results show that Hydrus 1D closely simulated soil water content at five depths as compared to water content measurements from soil moisture probes, by stepwise calibration and local sensivity analysis and optimization the Ks, n and α value in the calibration and optimization analysis. The errors of the model, expressed by deviations between observed and modeled soil water content were, however, different for each individual depth. The smallest differences between the observed value and soil-water content were attained when using an automated inverse optimization method. The choice of the initial parameter value can be optimized using a stepwise approach. Our results show that statistical evaluation coefficients (R2, Ce and RMSE) are suitable benchmarks to evaluate the performance of the model in reproducing the data. The degree of water stress simulated with Hydrus 1D suggested to increase irrigation at least one time, i.e. at the beginning of the simulation period and further distribute the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season. In the next year, we will further look for to the best method (using soft data and methods for instance PTFs, EMI, Penetrometer) to derive and predict the spatial variability of soil hydraulic properties (saturated hydraulic conductivity) of the soil and link to crop yield at the field scale. Linear and non-linear pedotransfer functions (PTFs) have been assessed to predict penetrometer resistance of soils from their water status (matric potential, ψ and degree of saturation, S) and bulk density, ρb, and some other soil properties such as sand content, Ks etc. The geophysical EMI (electromagnetic induction) technique provides a versatile and robust field instrument for determining apparent soil electrical conductivity (ECa). ECa, a quick and reliable measurement, is one of ancillary properties (secondary information) of soil, can improve the spatial and temporal estimation of soil characteristics e.g., salinity, water content, texture, prosity and bulk density at different scales and depths. According to previous literature on penetrometer measurements, we determined the effective stress and used some models to find the relationships between soil properties, especially Ks, and penetrometer resistance as one of the prediction methods for Ks. The initial results obtained in the first yearshowed that a new data set would be necessary to validate the results of this part. In the third year, quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set -up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, groundwater depth, root zone depth). The measured soil properties are extrapolated over the entire field by linking them to the available spatially distributed data (such as the EMI-images). The data set of predicted Ks and other soil properties for the whole field constructed in the previous steps will be used for parameterising the model. Sensitivity analysis ‘SA’ is essential to the model optimization or parametrization process. To avoid overparameterization, the use of global sensitivity analysis (SA) will be investigated. In order to include multiple objectives (irrigation management parameters, costs, …) in the parameter optimization strategy, multi-objective techniques such as AMALGAM have been introduced. We will investigate multi-objective strategies in the irrigation optimization

Detection of Radial Surface Brightness Fluctuation and Color Gradients in elliptical galaxies with ACS

We study surface brightness fluctuations (SBF) in a sample of 8 elliptical galaxies using Advanced Camera for Surveys (ACS) Wide Field Channel (WFC) data drawn from the Hubble Space Telescope (HST) archive. SBF magnitudes in the F814W bandpass, and galaxy colors from F814W, F435W, and F606W images -- when available -- are presented. Galaxy surface brightness profiles are determined as well. We present the first SBF--broadband color calibration for the ACS/WFC F814W bandpass, and (relative) distance moduli estimates for 7 of our galaxies. We detect and study in detail the SBF variations within individual galaxies as a probe of possible changes in the underlying stellar populations. Inspecting both the SBF and color gradients in comparison to model predictions, we argue that SBF, and SBF-gradients, can in principle be used for unraveling the different evolutionary paths taken by galaxies, though a more comprehensive study of this issue would be required. We confirm that the radial variation of galaxy stellar population properties should be mainly connected to the presence of radial chemical abundance gradients, with the outer galaxy regions being more metal poor than the inner ones.Comment: 47 pages, 13 figures, ApJ, accepte

The ALHAMBRA Survey: Bayesian Photometric Redshifts with 23 bands for 3 squared degrees

The ALHAMBRA (Advance Large Homogeneous Area Medium Band Redshift Astronomical) survey has observed 8 different regions of the sky, including sections of the COSMOS, DEEP2, ELAIS, GOODS-N, SDSS and Groth fields using a new photometric system with 20 contiguous ~ $300\AA$ filters covering the optical range, combining them with deep $JHKs$ imaging. The observations, carried out with the Calar Alto 3.5m telescope using the wide field (0.25 sq. deg FOV) optical camera LAICA and the NIR instrument Omega-2000, correspond to ~700hrs on-target science images. The photometric system was designed to maximize the effective depth of the survey in terms of accurate spectral-type and photo-zs estimation along with the capability of identification of relatively faint emission lines. Here we present multicolor photometry and photo-zs for ~438k galaxies, detected in synthetic F814W images, complete down to I~24.5 AB, taking into account realistic noise estimates, and correcting by PSF and aperture effects with the ColorPro software. The photometric ZP have been calibrated using stellar transformation equations and refined internally, using a new technique based on the highly robust photometric redshifts measured for emission line galaxies. We calculate photometric redshifts with the BPZ2 code, which includes new empirically calibrated templates and priors. Our photo-zs have a precision of $dz/(1+z_s)=1$ for I<22.5 and 1.4% for 22.5<I<24.5. Precisions of less than 0.5% are reached for the brighter spectroscopic sample, showing the potential of medium-band photometric surveys. The global $P(z)$ shows a mean redshift =0.56 for I=0.86 for I<24.5 AB. The data presented here covers an effective area of 2.79 sq. deg, split into 14 strips of 58.5'x15.5' and represents ~32 hrs of on-target.Comment: The catalog data and a full resolution version of this paper is available at https://cloud.iaa.csic.es/alhambra

Minimax rank estimation for subspace tracking

Rank estimation is a classical model order selection problem that arises in a variety of important statistical signal and array processing systems, yet is addressed relatively infrequently in the extant literature. Here we present sample covariance asymptotics stemming from random matrix theory, and bring them to bear on the problem of optimal rank estimation in the context of the standard array observation model with additive white Gaussian noise. The most significant of these results demonstrates the existence of a phase transition threshold, below which eigenvalues and associated eigenvectors of the sample covariance fail to provide any information on population eigenvalues. We then develop a decision-theoretic rank estimation framework that leads to a simple ordered selection rule based on thresholding; in contrast to competing approaches, however, it admits asymptotic minimax optimality and is free of tuning parameters. We analyze the asymptotic performance of our rank selection procedure and conclude with a brief simulation study demonstrating its practical efficacy in the context of subspace tracking.Comment: 10 pages, 4 figures; final versio

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction

Experimental designs for environmental valuation with choice-experiments: A Monte Carlo investigation

We review the practice of experimental design in the environmental economics literature concerned with choice experiments. We then contrast this with advances in the field of experimental design and present a comparison of statistical efficiency across four different experimental designs evaluated by Monte Carlo experiments. Two different situations are envisaged. First, a correct a priori knowledge of the multinomial logit specification used to derive the design and then an incorrect one. The data generating process is based on estimates from data of a real choice experiment with which preference for rural landscape attributes were studied. Results indicate the D-optimal designs are promising, especially those based on Bayesian algorithms with informative prior. However, if good a priori information is lacking, and if there is strong uncertainty about the real data generating process - conditions which are quite common in environmental valuation - then practitioners might be better off with conventional fractional designs from linear models. Under misspecification, a design of this type produces less biased estimates than its competitors