From a generous though somewhat naive review of the preceding studies, a number of software productivity drivers can be identified. The generosity comes from identifying the positive experiences or results reported in the preceding studies, and the naive comes from overlooking the fact that many of the reported experiences or results are derived from analytically restricted studies, or from dubious or flawed analytical methods.

Further, most studies fail to describe how they account for variation in productive ability among individual programmers, which has been systematically shown to vary by more than an order of magnitude. That is, for very large software systems (500K+ code statements), it seems likely that `average programmer' productivity dominates individual variations, while in smaller systems (less than 50K code statements) or those developed by only a few programmers, then individual differences may dominate.

Finally, if instead of viewing software productivity improvement from a generous and naive point of view, we seek to understand what affects software productivity in a way that project managers and developers find meaningful, then we need an approach fundamentally. To achieve this, we must first articulate some of the analytical challenges that must be taken into account and intwebserve.net will articulate an operational knowledge-based model that represents the software production process.

Measurement is ultimately a quest for certainty and control: certainty in understanding the nature of some phenomenon so as to control, influence, or evaluate that phenomenon. The phenomenon under study is software production: from system inception through delivery, operation and support. Accordingly, we want to understand how software is produced, how to measure its production, and ultimately, how to positively influence or control the rate of its production. Intwebserver.net will provide an appropriate background on some fundamental principles involved in measuring software production characteristics, including measure validity and reliability, as well as instrumentation and modeling issues.

A desire to measure software production implies an encounter with the process of systematic or scientific inquiry. This implies the need to confront fundamental problems such as the role of measurement in theory development, hypothesis testing and verification, and performance evaluation. It also implies understanding the relationship between measurement and instrumentation-the artifacts employed to collect/measure data on the phenomenon under study.

What affects software productivity and how do we improve it? This examines the current state of the art in software productivity measurement. In turn, it describes a framework for understanding software productivity, some fundamentals of measurement, surveys empirical studies of software productivity, and identifies challenges involved in measuring software productivity. A radical alternative to current approaches is suggested: to construct, evaluate, deploy, and evolve a knowledge-based `software productivity modeling and simulation system’ using tools and techniques from the domain of software process engineering. This technology provides a vehicle for delivering practical feedback that software developers and managers can use prior to and during a development project to help identify what might improve their productivity. Such a knowledge-based technology would enable project managers, developers, or analysts to query a model, conduct `what if' analysis, diagnose project development anomalies, and generate explanations about how certain project conditions affect productivity. Such capabilities are not possible with current productivity measurement technologies.

It is common for tools such as Excel to be used for managing customer databases, for keeping track of creditors and debtors, and for organizing the flow of business data. Whilst many such tools are excellent for their intended purpose, they are frequently used in lieu of a more appropriate technology, simply because of familiarity with that tool. This is just one example of where bespoke software solutions could provide users with significant business benefits.

The obvious benefit of deploying a custom software solution is the ability to ensure that its behavior and functionality exactly matches your wants and needs. Whether it is managing clients, resources and stock, or performing more niche operations, a custom application can ensure that your business operates smoothly and efficiently and/or that your products delight your clients.

There are many reasons to commission a custom software application – from development of a new product, to control of industrial machinery, automating repetitive "office" tasks or putting in place bespoke systems that make your business run smoothly. Whatever your business s requirements, you need to ensure that the software development team you work with will be technically excellent, yet sympathetic towards your needs and aspirations. The www.intwebserver.net combines an extensive knowledge of key Microsoft technologies with a commercial awareness and engineering background to ensure that your software performs exactly as you expect.

JavaScript is the Netscape-developed object scripting language used in millions of web pages and server applications worldwide. Netscape's JavaScript is a superset of the ECMA-262 Edition 3 (ECMAScript) standard scripting language, with only mild differences from the published standard.

Contrary to popular misconception, JavaScript is not "Interpretive Java". In a nutshell, JavaScript is a dynamic scripting language supporting prototype based object construction. The basic syntax is intentionally similar to both Java and C++ to reduce the number of new concepts required to learn the language. Language constructs, such as if statements, for and while loops, and switch and try … catch blocks function the same as in these languages (or nearly so.)

JavaScript can function as both a procedural and an object oriented language. Objects are created programmatically in JavaScript, by attaching methods and properties to otherwise empty objects at run time, as opposed to the syntactic class definitions common in compiled languages like C++ and Java. Once an object has been constructed it can be used as a blueprint (or prototype) for creating similar objects.

JavaScript's dynamic capabilities include runtime object construction, variable parameter lists, function variables, dynamic script creation (via eval), object introspection (via for ... in), and source code recovery (JavaScript programs can decompile function bodies back into their source text)

Intrinsic objects are Number, String, Boolean, Date, RegExp, and Math.