software engineering primer (6 résultats)

Etat : New.

Taschenbuch. Etat : Neu. Exercising machine language paradigms in software engineering | A Quick Primer | Manas Kumar Yogi (u. a.) | Taschenbuch | 52 S. | Englisch | 2019 | Scholars' Press | EAN 9786138914723 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

LeatherBound. Etat : New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. LeatherBound edition. Condition: New. Reprinted from 1982 edition. Leather Binding on Spine and Corners with Golden leaf printing on spine. Bound in genuine leather with Satin ribbon page markers and Spine with raised gilt bands. A perfect gift for your loved ones. Pages: 572 NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Pages: 572.

Etat : New. Print on Demand.

Etat : New. PRINT ON DEMAND.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Precise estimation of software development exertion is basic in software designing. Thinks little of lead to time weights that may bargain full useful development and intensive testing of software. Interestingly, overestimates can bring about noncompetitive contract offers as well as over designation of development assets and work force. Thus, numerous models for assessing software development exertion have been proposed. This work portrays principles of machine learning, which we use to manufacture estimators of software development exertion from recorded information. Our work demonstrates that these strategies are focused with conventional estimators on one dataset, yet additionally delineate that these techniques are delicate to the information on which they are prepared. This preventative note applies to any model-development procedure that depends on authentic information. Every single such model for software exertion estimation ought to be assessed by investigating model affectability on an assortment of authentic information.