No industry is as global as software development.  Pervasive networking means that software developers can, and do, work from anywhere. This has led many businesses to hiring development subcontractors in other countries, aiming to find good development talent at lower prices, or with fewer hassles on entry into the US.

While this is an ongoing and dynamic equilibrium, there are compelling reasons for doing software development in the United States, or using a hybrid model where some parts of the task are parceled out to foreign contractors and some are handled locally.

Development Methodologies

The primary reason for developing software overseas is cost reduction. The primary argument against overseas software development is slower development cycles. When software still used the "waterfall" industrial process for project management (where everything is budgeted in terms of time at the beginning of the project), offshoring was quite compelling. As more companies emulate Google and Facebook's process of "release early, update often, and refine from user feedback," an increasing premium has been put on software teams that are small enough to be agile (indeed, the development process is called Agile Development), and centralized enough, in terms of time zones, that collaborators can work together. This has made both Google and Facebook leaders in US-based software development, though they both still maintain teams of developers in other countries tasked with specific projects.

Localization For Americans

The United States is still one of the major markets for software development, and projects aimed at American customers needs to meet cultural norms. This applies to any country, not just the U.S. This puts a premium on software developers who aren't just fluent in English, but native speakers, and who understand American culture. While it's possible (and even likely) to make server-side software, and management utilities that can get by with terse, fractured English, anything that's enterprise-facing or consumer-facing requires more work on polish and presentation than is practical using outsourced developers. There is a reason why the leaders in software User Interface development are all US-based companies, and that's because consumer-focused design is still an overwhelming US advantage.

Ongoing Concerns

The primary concern for American software development is talent production. The US secondary education system produces a much smaller percentage of students with a solid math and engineering background, and while US universities lead the world in their computer science and engineering curricula, slightly under half of all of those graduates are from foreign countries, because American students don't take the course loads needed to succeed in them. Software development companies in the United States are deeply concerned about getting enough engineers and programmers out of the US university system. Some, such as Google, are trying to get programmers hooked on logical problem solving at a young age, with the Summer of Code programs. Others, like Microsoft, offer scholarships for computer science degrees.

Overall, the changes in project management methodologies mean that the US is the current leader in software development, and so long as the primary market for software remains English and American-centric, that's going to remain true. That trend is far from guaranteed, and in the world of software, things can change quickly.

Once again Java tops C as the number one sought after programming language on the internet.  According TIOBE Programming Community Index for February 2013 and five search engines: Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube and Baidu, Java regained its position after being bumped by C in May 2012.

Despite the recent urging by the U.S. Department of Homeland Security of computer users to disable or uninstall Java due to a flaw in Runtime Environment (JRE) 7, Java, has increased its market share of all languages by (+2.03%) in the past six months. The jump in Java’s popularity does not come as a surprise as the Android OS claims massive success in the mobile space.  The top twelve programming languages listed in the index are:

1.  Java
2.  C
3.  Objective-C 
4.  C++
5.  C#
6.  PHP
7.  Python
8.  (Visual) Basic
9.  Perl
10.  Ruby
11. Java Script
12. Visual Basic.NET

Also rising, Python and PHP which are competing to becoming the most popular interpreted language.

The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.

Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.

Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.

Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.

This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.

Related:

Who Are the Main Players in Big Data?

I suspect that many of you are familiar with the term "hard coding a value" whereby the age of an individual or their location is written into the condition (or action) of a business rule (in this case) as shown below:

if customer.age > 21 and customer.city == 'denver'

then ...

Such coding practices are perfectly expectable provided that the conditional values, age and city, never change. They become entirely unacceptable if a need for different values could be anticipated. A classic example of where this practice occurred that caused considerable heartache in the IT industry was the Y2K issue where dates were updated using only the last 2 digits of a four digit number because the first 2 digits were hard-coded to 19 i.e. 1998, 1999. All was well provided that the date did not advance to a time beyond the 1900’s since no one could be certain of what would happen when the millennia arrived (2000). A considerably amount of work (albeit boring) and money, approximately $200 billion, went into revising systems by way of software rewrites and computer chip replacements in order to thwart any detrimental outcomes. It is obvious how a simple change or an assumption can have sweeping consequences.

You may wonder what Y2K has to do with Business Rule Management Systems (BRMS). Well, what if we considered rules themselves to be hard-coded. If we were to write 100s of rules in Java, .NET or whatever language that only worked for a given scenario or assumption, would that not constitute hard-coded logic? By hard-coded, we obviously mean compiled. For example, if a credit card company has a variety of bonus campaigns, each with their own unique list of rules that may change within a week’s time, what would be the most effective way of writing software to deal with these responsibilities?