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Software construction is a software engineering discipline. It is the detailed creation of working meaningful software through a combination of coding, verification, unit testing, integration testing, and debugging. It is linked to all the other software engineering disciplines, most strongly to software design and software testing.
In the context of software systems and software engineering, system integration testing is a testing process that exercises a software system's coexistence with others. With multiple integrated systems, assuming that each have already passed system testing, [ 2 ] SIT proceeds to test their required interactions.
The cleanroom software engineering process is a software development process intended to produce software with a certifiable level of reliability. The central principles are software development based on formal methods, incremental implementation under statistical quality control, and statistically sound testing.
Software crisis is a term used in the early days of computing science for the difficulty of writing useful and efficient computer programs in the required time. The software crisis was due to the rapid increases in computer power and the complexity of the problems that could be tackled.
The Personal Software Process (PSP) is a structured software development process that is designed to help software engineers better understand and improve their performance by bringing discipline to the way they develop software and tracking their predicted and actual development of the code. It clearly shows developers how to manage the ...
Online tutoring is the process of tutoring in an online, virtual, or networked, environment, in which teachers and learners participate from separate physical locations. [1] ...
Software-defined networking (SDN) is an approach to network management that enables dynamic and programmatically efficient network configuration to improve network performance and monitoring in a manner more akin to cloud computing than to traditional network management. [1]
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1] [2] [3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.