By Craig Larman
Larman outlines the rules and top practices of iterative, evolutionary, and agile techniques to software program improvement that emphasize collaboration and suppleness, illustrates these practices in an instance procedure for monitoring immigrants, and overviews the paintings items and center practices of the Scrum procedure, severe programming, the unified method, and evolutionary undertaking administration (Evo).
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Additional resources for Agile and Iterative Development (Agile Software Development Series): A Manager's Guide
The average case within organizations is relatively high failure rates and other undesirable project qualities [Standish00]; it is in this context that IID methods are worth considering. failure research Failure has several meanings. For example, at Symantec (now adopting XP on some projects), the company was attempting a waterfall model. Tight project management resulted in delivering a product on time and in budget, but it was the wrong product [Bowers02]. They adopted XP, which led to a better targeted product; productivity and defect rates also improved.
Requirements change 25% or more [Jones97, BP88]. Thus, we have the requirements challenge, We want the requirements to be stable, but they aren't. Attempts to face this challenge by early detailed requirements analysis and freeze can rarely succeed, given the high rates of change. In a study of failure factors on over 1,000 software projects [Thomas01], such practices were associated with the largest contributing factor for failure, being cited in 82% of the projects as the number one problem.
Success vs. 2). 2. success vs. 5M-3M 3M-6M 6M-10M > 10M Success 68% 22% 9% 1% 0% And, to reiterate a portion of the Standish conclusion, … shorter time frames, with delivery of software components early and often, increase the success rate. Another interesting research note on size in the Standish research was the declining project failure rates, from 31% in the 1994 study to 23% in the 2000 study. This was correlated with smaller, shorter projects and smaller teams. Direct smaller-size and evolutionary delivery evidence was presented in a previously cited study [CLW01].