Evidence-based Software Engineering

Show simple item record

dc.contributor.author	Derek, Jones
dc.date.accessioned	2022-10-03T05:35:05Z
dc.date.available	2022-10-03T05:35:05Z
dc.date.issued	2020
dc.identifier.isbn	13: 9781838291303
dc.identifier.uri	http://oer.just.edu.jo/xmlui/handle/123456789/345
dc.description	This book discusses what is currently known about software engineering based on an analysis of all publicly available software engineering data. This aim is not as ambitious as it sounds because there is not a lot of data publicly available. The analysis is like a join-the-dots puzzle, except that the 600+ dots are not numbered, some of them are actually specs of dust, and many dots are likely to be missing. The way forward is to join the dots to build an understanding of the processes involved in building and maintaining software systems; work is also needed to replicate some of the dots to confirm that they are not specs of dust, and to discover missing dots. The dots are sprinkled across chapters covering the major issues involved in building and maintaining a software system; when dots could be applicable to multiple issues your author selected the issue he felt maximised the return on use. If data relating to a topic is not publicly available, that topic is not discussed. Adhering to this rule has led to a very patchy discussion, although it vividly highlights the almost non-existent evidence for current theories of software development. The intended audience is software developers and their managers. Some experience of building software systems is assumed. The material is in two parts, one covering software engineering and the second introduces analysis techniques applicable to the analysis of software engineering data. 1. Economic factors motivate the creation of software systems, which are a product of human cognitive effort; these two factors underpin any analysis of software engineering processes. Software development has progressed in to the age of the ecosystemi successfully building a software system is dependent on a team capable of effectively selecting the libraries and packages providing the algorithms that are good enough to get the job done, write code when necessary, and to interface to a myriad of services and other software systems, 2. Developers are casual users of statistics and don’t want to spend time learning lots of mathematics; they want to use the techniques, not implement them. The material assumes the reader has some basic mathematical skills, e.g., knows a little about probability, permutations, and the idea of measurements containing some amount of error. It is assumed that developer time is expensive and computer time is cheap. Where possible a single, general, analysis technique is described, and a single way of coding something in R is consistently used. R was chosen as the language/ecosystem for statistical analysis because of its extensive ecosystem; there are many books, covering a wide range of subject areas, and active online forums discussing R usage.	en_US
dc.language.iso	en	en_US
dc.publisher	Knowledge Software	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/	*
dc.subject	Software Engineering	en_US
dc.title	Evidence-based Software Engineering	en_US
dc.type	Book	en_US