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This paper introduces a requirements entropy framework (REF) for measuring requirements trends and estimating engineering effort during system development.
The REF treats the requirements engineering process as an open system in which the total number of requirements R transition from initial states of high requirements entropy HR, disorder and uncertainty toward the desired end state of inline image as R increase in quality.
The cumulative requirements quality Q reflects the meaning of the requirements information in the context of the SE problem.
The distribution of R among N discrete quality levels is determined by the number of quality attributes accumulated by R at any given time in the process. The number of possibilities P reflects the uncertainty of the requirements information relative to inline image. The HR is measured or estimated using R, N and P by extending principles of information theory and statistical mechanics to the requirements engineering process.
The requirements information I increases as HR and uncertainty decrease, and ΔI is the additional information necessary to achieve the desired state from the perspective of the receiver. The HR may increase, decrease or remain steady depending on the degree to which additions, deletions and revisions impact the distribution of R among the quality levels.
Current requirements volatility metrics generally treat additions, deletions and revisions the same and simply measure the quantity of these changes over time. The REF measures the quantity of requirements changes over time, distinguishes between their positive and negative effects in terms of inline image, and ΔI, and forecasts when a specified desired state of requirements quality will be reached, enabling more accurate assessment of the status and progress of the engineering effort.
Results from random variable simulations suggest the REF is an improved leading indicator of requirements trends that can be readily combined with current methods. The additional engineering effort ΔE needed to transition R from their current state to the desired state can also be estimated. Simulation results are compared with measured engineering effort data for Department of Defense programs, and the results suggest the REF is a promising new method for estimating engineering effort for a wide range of system development programs
How do you work with a team that has a low tolerance for high change or uncertainty?
"To create and grow an enterprise like Amazon or Uber takes a certain libertarian cowboy mind-set that ignores obstacles and rules.Certainly, the German view of American business practices is the antithesis of following the central plan. To me, this is not all that unfamiliar since resistance to central planning, state oversight, and the admiration for the "cowboy spirit" of individualism is culturally mainstream in the U.S., less so in the social democracies.
Silicon Valley fears neither fines nor political reprimand. It invests millions in lobbying in Brussels and Berlin, but since it finds the democratic political process too slow, it keeps following its own rules in the meantime. .....
It is this anarchical spirit that makes Germans so neurotic [about American technology impacts on society]. On one hand, we’d love to be more like that: more daring, more aggressive. On the other hand, the force of anarchy makes Germans (and many other Europeans) shudder, and rightfully so. It’s a challenge to our deeply ingrained faith in the state.
... blaming health workers who contract Ebola sidesteps the statistical elephant in the room: The protocol ... appears not to recognize the probabilities involved as the number of contacts between health workers and Ebola patients continues to grow.
This is because if you do something once that has a very low probability of a very negative consequence, your risks of harm are low. But if you repeat that activity many times, the laws of probability ... will eventually catch up with you.
We all know we must estimate with three points (numbers)... so we do it, reluctantly
None of us actually want to work with (do arithmetic with) or work to (be accountable to) the three points we estimate
We all want accurate estimates backed up by data
But data -- good or bad -- may not be the driver for accurate estimates
Psychological and political explanations better account for inaccurate forecasts.
Psychological explanations account for inaccuracy in terms of optimism bias; that is, a cognitive predisposition found with most people to judge future events in a more positive light than is warranted by actual experience.
Political explanations, on the other hand, explain inaccuracy in terms of strategic misrepresentation.