AI News, Difference between revisions of "Computer Programming"

Difference between revisions of "Computer Programming"

' is the craft of writing useful, maintainable, and extensible source code which can be interpreted or compiled by a computing system to perform a meaningful task.

Programming a computer can be performed in one of numerous languages, ranging from a higher-level language to writing directly in low-level machine code (that is, code that more directly controls the specifics of the computer's hardware) all the way down to writing microcode (which does directly control the electronics in the computer).

Computer programming is one part of a much larger discipline known as software engineering, which includes several different aspects of making software including design, construction and quality control.

Whereas software engineering is interested specifically in making software, computer science tends to be oriented towards more theoretical or mathematical problems.

Unfortunately the employment market has contributed greatly to misconceptions about computer programming by companies advertising for employees with a specific (therefore limited) computer language skill-set and responses being handled by human resources(HR), without someone with a programming background.

An algorithm is a list of well-defined instructions for completing a task, and knowing several languages means having the ability to list the computer instructions in many different ways.

In one sense this is true because all digital electronic computers translate programming languages into strings of ones and zeros called binary, or Machine code.

While mainstream, personal computer languages tend to be derived from a specific tradition and are very similar (hence the popularity of this misconception), some languages fall into different paradigms which provide for a radically different programming experience.

Imperative and object-oriented languages tend to be used in the mainstream, whereas functional and declarative languages tend to be used in academic settings.

We do not make any claims about who is right on this matter, but at the very least, we will suggest that building familiarity with the four major paradigms is an extremely valuable exercise.

There are low-level and high-level languages, the difference between the two is that low-level languages often use 0s and 1s, and this works because it gives the computer the ability to quickly understand what needs to be done or executed.

For a list including various computer languages arranged together by syntax terms and patterns, see Wikipedia's lists of computer syntax patterns.

Luckily for you, many of these problems have been studied by computer scientists for a very long time, sometimes leading to provably unbeatable solutions, or sometimes solutions which are 'good enough' for every day needs.

Computer programming

Computer programming is the process of designing and building an executable computer program for accomplishing a specific computing task.

Programming involves tasks such as analysis, generating algorithms, profiling algorithms' accuracy and resource consumption, and the implementation of algorithms in a chosen programming language (commonly referred to as coding[1][2]).

Related tasks include testing, debugging, maintaining a program's source code, implementation of build systems, and management of derived artifacts such as machine code of computer programs.

These might be considered part of the programming process, but often the term software development is used for this larger process with the term programming, implementation, or coding reserved for the actual writing of source code.

Later a control panel (plugboard) added to his 1906 Type I Tabulator allowed it to be programmed for different jobs, and by the late 1940s, unit record equipment such as the IBM 602 and IBM 604, were programmed by control panels in a similar way;

Assembly languages were soon developed that let the programmer specify instruction in a text format, (e.g., ADD X, TOTAL), with abbreviations for each operation code and meaningful names for specifying addresses.

High-level languages allow the programmer to write programs in terms that are syntactically richer, and more capable of abstracting the code, making it targetable to varying machine instruction sets via compilation declarations and heuristics.

Readability is important because programmers spend the majority of their time reading, trying to understand and modifying existing source code, rather than writing new source code.

The presentation aspects of this (such as indents, line breaks, color highlighting, and so on) are often handled by the source code editor, but the content aspects reflect the programmer's talent and skills.

The academic field and the engineering practice of computer programming are both largely concerned with discovering and implementing the most efficient algorithms for a given class of problem.

For this purpose, algorithms are classified into orders using so-called Big O notation, which expresses resource use, such as execution time or memory consumption, in terms of the size of an input.

The first step in most formal software development processes is requirements analysis, followed by testing to determine value modeling, implementation, and failure elimination (debugging).

Many programmers use forms of Agile software development where the various stages of formal software development are more integrated together into short cycles that take a few weeks rather than years.

the number of books sold and courses teaching the language (this overestimates the importance of newer languages), and estimates of the number of existing lines of code written in the language (this underestimates the number of users of business languages such as COBOL).

New languages are generally designed around the syntax of a prior language with new functionality added, (for example C++ adds object-orientation to C, and Java adds memory management and bytecode to C++, but as a result, loses efficiency and the ability for low-level manipulation).

When debugging the problem in a GUI, the programmer can try to skip some user interaction from the original problem description and check if remaining actions are sufficient for bugs to appear.

Trade-offs from this ideal involve finding enough programmers who know the language to build a team, the availability of compilers for that language, and the efficiency with which programs written in a given language execute.

How to Become a Computer Programmer: Computer Programming Degrees & Careers

A bachelor’s degree is usually the minimum educational requirement in this field, as it demonstrates to employers that students have not only a broad range of experience with programming languages and concepts, but also that they have developed the problem-solving skills that are vital to this type of employment.

Instead, programming is included as an integral component in a bachelor’s degree in a related area, such as the ones mentioned above—software engineering, computer science, information technology, information systems security and computer engineering—or a similar field.

Additionally, students will gain a working knowledge of computer systems and technology, as well as get hundreds of hours of practice using multiple programming languages, designing databases and creating Web applications.

For students who choose not to continue on to a bachelor’s degree, such skills will help guide their future learning as they build their knowledge on-the-job, becoming proficient in specific programming languages.

Specifically, students become proficient in discrete math—a branch of mathematics that deals with objects that have distinct values—as well as computer organization and architecture, algorithms, programming and software design.

The bachelor’s degree courses listed below show the types of skills students can expect to gain at this level: As with a bachelor’s degree, there is no specific computer programming degree at the master’s level.

Because programming requires a solid foundation in math, logic and computer engineering and architecture, the following degree options are ideal paths for aspiring computer programmers: Computer programming degrees are ideal for online study.

Computer Programmers

Computer and information research scientists invent and design new approaches to computing technology and find innovative uses for existing technology.

Computer and information systems managers, often called information technology (IT) managers or IT project managers, plan, coordinate, and direct computer-related activities in an organization.

Computer hardware engineers research, design, develop, and test computer systems and components such as processors, circuit boards, memory devices, networks, and routers.

Computer network architects design and build data communication networks, including local area networks (LANs), wide area networks (WANs), and Intranets.

These networks range from small connections between two offices to next-generation networking capabilities such as a cloud infrastructure that serves multiple customers.

Computer systems analysts, sometimes called systems architects, study an organization's current computer systems and procedures, and design solutions to help the organization operate more efficiently and effectively.

The Coming Software Apocalypse

Still, most software, even in the safety-obsessed world of aviation, is made the old-fashioned way, with engineers writing their requirements in prose and programmers coding them up in a programming language like C.

Margaret Hamilton, a celebrated software engineer on the Apollo missions—in fact the coiner of the phrase “software engineering”—told me that during her first year at the Draper lab at MIT, in 1964, she remembers a meeting where one faction was fighting the other about transitioning away from “some very low machine language,” as close to ones and zeros as you could get, to “assembly language.” “The people at the lowest level were fighting to keep it.

No wonder, he said, that “people are not so easily transitioning to model-based software development: They perceive it as another opportunity to lose control, even more than they have already.” The bias against model-based design, sometimes known as model-driven engineering, or MDE, is in fact so ingrained that according to a recent paper, “Some even argue that there is a stronger need to investigate people’s perception of MDE than to research new MDE technologies.” Which sounds almost like a joke, but for proponents of the model-based approach, it’s an important point: We already know how to make complex software reliable, but in so many places, we’re choosing not to.

“Human intuition is poor at estimating the true probability of supposedly ‘extremely rare’ combinations of events in systems operating at a scale of millions of requests per second,” he wrote in a paper.

the code faithfully implements the intended design, but the design fails to correctly handle a particular ‘rare’ scenario.” Newcombe was convinced that the algorithms behind truly critical systems—systems storing a significant portion of the web’s data, for instance—ought to be not just good, but perfect.

This is why he was so intrigued when, in the appendix of a paper he’d been reading, he came across a strange mixture of math and code—or what looked like code—that described an algorithm in something called “TLA+.” The surprising part was that this description was said to be mathematically precise: An algorithm written in TLA+ could in principle be proven correct.

That is, before you write any code, you write a concise outline of your program’s logic, along with the constraints you need it to satisfy (say, if you were programming an ATM, a constraint might be that you can never withdraw the same money twice from your checking account).

Code makes you miss the forest for the trees: It draws your attention to the working of individual pieces, rather than to the bigger picture of how your program fits together, or what it’s supposed to do—and whether it actually does what you think.

Because they never learned it.” Lamport sees this failure to think mathematically about what they’re doing as the problem of modern software development in a nutshell: The stakes keep rising, but programmers aren’t stepping up—they haven’t developed the chops required to handle increasingly complex problems.

“In the 15th century,” he said, “people used to build cathedrals without knowing calculus, and nowadays I don’t think you’d allow anyone to build a cathedral without knowing calculus.

And I would hope that after some suitably long period of time, people won’t be allowed to write programs if they don’t understand these simple things.” Newcombe isn’t so sure that it’s the programmer who is to blame.

For one thing, he said that when he was introducing colleagues at Amazon to TLA+ he would avoid telling them what it stood for, because he was afraid the name made it seem unnecessarily forbidding: “Temporal Logic of Actions” has exactly the kind of highfalutin ring to it that plays well in academia, but puts off most practicing programmers.

“They google, and they look on Stack Overflow” (a popular website where programmers answer each other’s technical questions) “and they get snippets of code to solve their tactical concern in this little function, and they glue it together, and iterate.” “And that’s completely fine until you run smack into a real problem.” In the summer of 2015, a pair of American security researchers, Charlie Miller and Chris Valasek, convinced that car manufacturers weren’t taking software flaws seriously enough, demonstrated that a 2014 Jeep Cherokee could be remotely controlled by hackers.

They took advantage of the fact that the car’s entertainment system, which has a cellular connection (so that, for instance, you can start your car with your iPhone), was connected to more central systems, like the one that controls the windshield wipers, steering, acceleration, and brakes (so that, for instance, you can see guidelines on the rearview screen that respond as you turn the wheel).

And while some of this code—for adaptive cruise control, for auto braking and lane assist—has indeed made cars safer (“The safety features on my Jeep have already saved me countless times,” says Miller), it has also created a level of complexity that is entirely new.

“I think the autonomous car might push them,” Ledinot told me—“ISO 26262 and the autonomous car might slowly push them to adopt this kind of approach on critical parts.” (ISO 26262 is a safety standard for cars published in 2011.) Barr said much the same thing: In the world of the self-driving car, software can’t be an afterthought.

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