Source Size Estimation
Source Size Estimation - Related Videos
06:00
Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
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Cited by 29 •
2011
Morphological scaling relationships capture and describe organismal shape. We present a method to measure morphological scaling relationships across the natural range of body sizes in fully metamorphic insects. Using a simple diet manipulation we increase the distribution of trait sizes, permitting the accurate description of how shape and size co-vary.
09:14
Preparing a Celadonite Electron Source and Estimating Its Brightness
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2019
The article presents a protocol to prepare a celadonite source and estimate its brightness for use in a long-range imaging low-energy electron point-source projection microscope.
01:25
The Anchoring-and-Adjustment Heuristic
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2020
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
08:25
BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams
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Cited by 2 •
2019
We present a simple and cost-effective method to build an open-source datalogger that measures the conductance of nonvascular cryptogams together with the environmental temperature and humidity. We describe the hardware design of the datalogger and provide step-by-step assembly instructions, the list of required open-source logging software, the code to run the datalogger, and a calibration protocol.
09:32
Cortical Source Analysis of High-Density EEG Recordings in Children
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Cited by 6 •
2014
In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.
FAQs
Reliability depends on how clearly requirements are decomposed, how well historical comparison projects match the new system, and whether the productivity model reflects the selected language, implementation complexity, and expected reuse. Poorly specified components or mismatched historical data can distort the predicted size. Engineers improve credibility by calibrating the model to relevant project experience.
Lines of code and statements describe implementation scale, whereas functional size offers a different basis tied to the system’s requirements. The choice affects how strongly results depend on language or design decisions and what information is available during estimation. Using a measure that fits the project helps engineers compare components consistently and select an appropriate productivity model.
These models translate an estimated amount of software into planning values such as effort, schedule, staffing, or cost. Calibration connects the model to historical project performance rather than relying on a generic productivity assumption. It also allows language, complexity, and reuse to influence the result, making the estimate more relevant to the engineering environment where the system will be developed.
Start by breaking the requirements into identifiable system components, then assign a size measure such as lines of code, statements, or functional size. Compare those components with historical projects and apply a calibrated productivity or parametric model. Record the resulting planning values and revisit them as design choices and implementation data clarify the system.
Revision is appropriate when design decisions change the expected implementation, when component boundaries become clearer, or when actual implementation data becomes available. Updating the estimate preserves its usefulness as a planning baseline instead of treating an early prediction as fixed. The revised result can then support more informed resource allocation and comparison with project progress.
They provide a common quantitative basis for considering effort, schedule, staffing, and cost together. A size estimate can help engineers examine whether planned resources align with the amount of software expected, while later updates show how emerging implementation information affects those plans. This makes estimation useful both before development and during progress tracking.