Insights on Research Techniques towards Cost Estimation in Software Design

Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript

Automatic Processing of High-Rate, High-Density Multibeam Echosounder Data

Multibeam echosounders (MBES) are currently the best way to determine the bathymetry of large regions of the seabed with high accuracy. They are becoming the standard instrument for hydrographic surveying and are also used in geological studies, mineral exploration and scientific investigation of the earth\u27s crustal deformations and life cycle. The significantly increased data density provided by an MBES has significant advantages in accurately delineating the morphology of the seabed, but comes with the attendant disadvantage of having to handle and process a much greater volume of data. Current data processing approaches typically involve (computer aided) human inspection of all data, with time-consuming and subjective assessment of all data points. As data rates increase with each new generation of instrument and required turn-around times decrease, manual approaches become unwieldy and automatic methods of processing essential. We propose a new method for automatically processing MBES data that attempts to address concerns of efficiency, objectivity, robustness and accuracy. The method attributes each sounding with an estimate of vertical and horizontal error, and then uses a model of information propagation to transfer information about the depth from each sounding to its local neighborhood. Embedded in the survey area are estimation nodes that aim to determine the true depth at an absolutely defined location, along with its associated uncertainty. As soon as soundings are made available, the nodes independently assimilate propagated information to form depth hypotheses which are then tracked and updated on-line as more data is gathered. Consequently, we can extract at any time a “current-best” estimate for all nodes, plus co-located uncertainties and other metrics. The method can assimilate data from multiple surveys, multiple instruments or repeated passes of the same instrument in real-time as data is being gathered. The data assimilation scheme is sufficiently robust to deal with typical survey echosounder errors. Robustness is improved by pre-conditioning the data, and allowing the depth model to be incrementally defined. A model monitoring scheme ensures that inconsistent data are maintained as separate but internally consistent depth hypotheses. A disambiguation of these competing hypotheses is only carried out when required by the user. The algorithm has a low memory footprint, runs faster than data can currently be gathered, and is suitable for real-time use. We call this algorithm CUBE (Combined Uncertainty and Bathymetry Estimator). We illustrate CUBE on two data sets gathered in shallow water with different instruments and for different purposes. We show that the algorithm is robust to even gross failure modes, and reliably processes the vast majority of the data. In both cases, we confirm that the estimates made by CUBE are statistically similar to those generated by hand

Operational Calibration: Debugging Confidence Errors for DNNs in the Field

Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field. A particularly damaging problem is that DNN models often give false predictions with high confidence, due to the unavoidable slight divergences between operation data and training data. To minimize the loss caused by inaccurate confidence, operational calibration, i.e., calibrating the confidence function of a DNN classifier against its operation domain, becomes a necessary debugging step in the engineering of the whole system. Operational calibration is difficult considering the limited budget of labeling operation data and the weak interpretability of DNN models. We propose a Bayesian approach to operational calibration that gradually corrects the confidence given by the model under calibration with a small number of labeled operation data deliberately selected from a larger set of unlabeled operation data. The approach is made effective and efficient by leveraging the locality of the learned representation of the DNN model and modeling the calibration as Gaussian Process Regression. Comprehensive experiments with various practical datasets and DNN models show that it significantly outperformed alternative methods, and in some difficult tasks it eliminated about 71% to 97% high-confidence (>0.9) errors with only about 10\% of the minimal amount of labeled operation data needed for practical learning techniques to barely work.Comment: Published in the Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020

The Economic Value of Remote Sensing of Earth Resources from Space: An ERTS Overview and the Value of Continuity of Service. Volume 1: Summary

An overview of the ERTS program is given to determine the magnitude of the benefits that can be reasonably expected to flow from an Earth Resources Survey (ERS) Program, and to assess the benefits foregone in the event of a one or two-year gap in ERS services. An independent evaluation of the benefits attributable to ERS-derived information in key application areas is presented. These include two case studies in agriculture-distribution, production and import/export, and one study in water management. The cost-effectiveness of satellites in an ERS system is studied by means of a land cover case study. The annual benefits achieveable from an ERS system are measured by the in-depth case studies to be in the range of $430 to$746 million. Benefits foregone in the event of a one-year gap in ERS service are estimated to be $147 to$220 million and $274 to$420 million for a two-year gap in ERS service

Initial specifications for van and aircraft

Proposed testing program for strapdown inertial system containing platform gyros and pendulous gyro

Statistical modelling of software reliability

During the six-month period from 1 April 1991 to 30 September 1991 the following research papers in statistical modeling of software reliability appeared: (1) A Nonparametric Software Reliability Growth Model; (2) On the Use and the Performance of Software Reliability Growth Models; (3) Research and Development Issues in Software Reliability Engineering; (4) Special Issues on Software; and (5) Software Reliability and Safety

Preferences for Health Insurance in Germany and the Netherlands – A Tale of Two Countries

This contribution contains an international comparison of preferences. Using two Discrete Choice Experiments (DCE), it measures willingness to pay for health insurance attributes in Germany and the Netherlands. Since the Dutch DCE was carried out right after the 2006 health reform, which made citizens explicitly choose a health insurance contract, two research questions naturally arise. First, are the preferences with regard to contract attributes (such as Managed-Care-type restrictions of physician choice) similar between the two countries? Second, was the information campaign launched by the Dutch government in the context of the reform effective in the sense of reducing status quo bias? Based on random-effects Probit estimates, these two questions can be answered as follows. First, while much the same attributes have positive and negative willingness to pay values in the two countries, their magnitudes differ, pointing to differences in preference structure. Second, status quo bias in the Netherlands is one-half of the German value, suggesting that Dutch consumers were indeed made to bear the cost of decision making associated with choice of a health insurance contract.preference measurement, willingness to pay, health insurance, discrete-choice experiments, health reform, Germany, Netherlands

Economic liberalisation targeted programmes and household food security

"Although there is little consensus on the impact of trade liberalization on poverty and food security, it is nevertheless widely acknowledged that there is a need for governments to establish safety-nets to guard against any potentially harmful effects on the poor and vulnerable sections of society. Against this background, programs aimed at achieving food security and reducing poverty gain increased importance in the reform era. This study aims to evaluate several such programs that are currently in place in the country from the point of view of their impact, efficiency and financial sustainability. The purpose is to determine how these programs may be improved and propose appropriate policy options for reform, while also keeping in mind the new challenges that might lie ahead. Specifically, the study evaluates the Public Distribution System (PDS), Public Works Programs, and certain food-based direct intervention programs such as the Integrated Child Development Scheme (ICDS) and Tamil Nadu Integrated Nutrition Program (TINP), with a view to suggest how they can be made more cost effectiveness and better targeted." From Author's Executive SummarySafety nets ,