Software engineers are not real engineers


There's also live online events, interactive content, certification prep materials, and more. Explore a preview version of Software Engineering at Google right now. Today, software engineers need to know not only how to program effectively but also how to develop proper engineering practices to make their codebase sustainable and healthy. This book emphasizes this difference between programming and software engineering. How can software engineers manage a living codebase that evolves and responds to changing requirements and demands over the length of its life?


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WATCH RELATED VIDEO: THIS IS NOT REAL - 'A dAy iN THe LiFe oF a SoFtWaRe EnGiNeEr' 1st Edition - #grindreel

How to Become a Software Engineer in 2022: A Guide for Those Starting From Scratch


As researchers investigate how software gets made, a new empire for empirical research opens up. DOI: Software engineering has long considered itself one of the hard sciences.

A growing number of researchers believe software engineering is now at a turning point comparable to the dawn of evidence-based medicine, when the health-care community began examining its practices and sorting out which interventions actually worked and which were just-so stories.

This burgeoning field is known as empirical software engineering and as interest in it has exploded over the past decade, it has begun to borrow and adapt research techniques from fields as diverse as anthropology, psychology, industrial engineering and data mining. Figure 1. Making software is a signature activity of our era that has produced a canon of beliefs and practices, yet only recently have the tools of social science and empirical investigation been systematically applied on a large scale to the software enterprise.

The software industry employs tens of millions of people worldwide; even small increases in their productivity could be worth billions of dollars a year.

And with software landing our planes, diagnosing our illnesses and keeping track of the wealth of nations, discovering how to make programs more reliable is hardly an academic question. Broadly speaking, people who study programming empirically come at the problem from one of two angles. To some, the phrase software engineering has always had a false ring.

Instead, programming is a skilled craft, more akin to architecture, which makes the human element an important some would say the important focus of study.

Those patterns can and should be examined using the empirical, often qualitative tools developed by the social and psychological sciences. Employing the same kinds of data-mining techniques that Amazon uses to recommend books and that astronomers use to find clusters of galaxies, software engineering researchers inspect these artifacts for patterns. Does the number of changes made to a program correlate with the number of bugs found in it? Does having more people work on a program make it better because more people have a chance to spot problems or worse because of communication stumbles?

All are designed to facilitate data sharing, amplifying the power of individual researchers. The questions we and our colleagues seek to answer are as wide-ranging as those an anthropologist might ask during first contact with a previously unknown culture. How do people learn to program? Can the future success of a programmer be predicted by personality tests?

Does the choice of programming language affect productivity? Can the quality of code be measured? Can data mining predict the location of software bugs? Is it more effective to design code in detail up front or to evolve a design week by week in response to the accretion of earlier code?

Convincing data about all of these questions are now in hand, and we are learning how to tackle many others. Along the way, our field is grappling with the fundamental issues that define any new science.

How do we determine the validity of data? When can conclusions from one context—one programming team, or one vast project, like the development of the Windows Vista operating system—be applied elsewhere? And crucially, which techniques are most appropriate for answering different kinds of questions?

A good example of the harvest that empirical studies are generating relates to one of the holy grails of software engineering: the ability to measure the quality of a program, not by running it and looking for errors, but by automated examination of the source code itself.

Any technique that could read a program and predict how reliable it would be before it is delivered to customers would save vast sums of money, and probably lives as well. Figure 2. There are many reasons to measure the complexity of computer code. The simplest metric of complexity for the snippet of code in a above, written in the C language, is the number of lines.

Next simplest is often the number of C functions; in the sample code here, one. Herraiz and Hassan tested whether any of the higher measures provided more diagnostic information about complexity than the simplest measure, lines of code.

Figure adapted from material in Herraiz, I. Beyond lines of code: Do we need more complexity metrics? One consistent discovery is that, in general, the more lines of code there are in a program, the more defects it probably has. This result may seem obvious, even trivial, but it is a starting point for pursuing deeper questions of code quality. Intuitively, programmers believe that some kinds of code are more complex than others, and that the more complex a piece of code is, the more likely it is to be buggy.

Can we devise some way to measure this complexity? And if so, can the location of complexity hot spots predict where defects will be found? One of the first attempts to answer this question was developed by Thomas J. McCabe and is known as cyclomatic complexity.

McCabe realized that any program can be represented as a graph whose arcs show the possible execution paths through the code. The simplest graph is a straight chain, which represents a series of statements with no conditions or loops.

Each if statement creates a parallel path through the graph; two such statements create four possible paths. Figure 2 shows a snippet of code extracted from a cross-platform download manager called Uget. The graph in part b shows the paths through the code; each if and loop adds one unit of complexity, giving this code an overall complexity score of 3. Instead of graph theory, it draws on information theory and is based on four easily measured features of code that depend on the number of distinct operators and operands, their total count, how easy they are to discriminate from one another and so on.

Figure 2 c shows the values for the sample piece of code in a. Hundreds of other metrics have been developed, published and analyzed over the past 30 years.

In their chapter, Herraiz and Hassan use statistical techniques to explore a simple question: Are any of these metrics actually better at predicting errors than simply counting the number lines of source code? Put another way, if a complexity metric is highly correlated with the number of lines of source code, does it actually provide any information that the simpler measure does not? For a case study, Herraiz and Hassan chose to examine the open-source Arch Linux operating system distribution, which yielded a sample of , unique source files in the C language.

They calculated the measures discussed above, and several others, for each of these files, taking special account of header files those consisting mainly of declarations that assist in code organization. They found that for nonheader files, where programs actually do their work, all the metrics tested showed a very high degree of correlation with lines of code. Checking for generalizability, the effect held for all but very small files. Like all negative results, this one is a bit disappointing.

Above all, the value here is in the progress of the science itself. The next time someone puts forward a new idea for measuring complexity, a validated, empirical test of its effectiveness will be there waiting for them. Metrics research looks at the code that programmers produce, but at least as much research effort has focused on how they produce it.

An interesting case is pair programming, a work style that burst onto the scene in the late s though people have been using it informally for as long as programming has existed. In pair programming, two programmers sit at a single workstation and create code together. The driver handles the keyboard and mouse, while the navigator watches, comments and offers corrections. Many programmers have noticed over the years that duos like this seem to produce code more quickly and with fewer bugs.

If asked, they would probably say that the benefits arise because different people naturally notice different sorts of things, or because not having to type gives the navigator more time to think, or possibly that having an audience makes the driver think more carefully. But is pair programming actually better, and if so, which of the possible explanations is the reason?

Figure 3. To the uninitiated, pair programming seems an out-there technique. Two programmers work at the same machine, usually with one piloting the keyboard, the other flying in the rear seat of the cockpit. Many innovators in software practices laud pair programming. Whether it should be preferred can be and has been investigated rigorously via a wide range of experiments that test different personality matches, different protocols for determining to whom control of the keyboard is assigned and different types of problems.

The first empirical study of pair programming, by Temple University professor John Nosek in , studied 15 programmers, 5 working alone the control group , the other 10 in 5 pairs. A challenging programming problem was given, with a time limit of 45 minutes. The results were statistically significant.

Solutions produced by the pairs took 60 percent more total time, but dividing the total time by two, they completed the tasks 20 percent faster. And the author of the study reported that the pairs produced higher-quality code. Subsequent studies with larger groups over longer periods of time, summarized by Laurie Williams at North Carolina State University, have expanded on these early results.

Pair programmers tend to produce code that is easier to understand, and they do so with higher morale. Their productivity may fall initially as the programmers adjust to the new work style, but productivity recovers and often surpasses its initial level as programmer teams acquire experience.

Related studies have also given insight into what actually happens during pair programming. Changing partners regularly seems to help, and swapping roles periodically between driver and navigator helps keep programmers engaged. These results can even influence hardware design.

Some teams have two people who both do best when they have control of the keyboard. Why not provide dual keyboards and mice? Despite the accumulation of evidence in its favor, pair programming is still very much the exception in industry. Managers and programmers alike often brush the data away, clinging to the idea that putting two people on one job must double staffing requirements and therefore cannot deliver efficiency.

This is an unfortunate phenomenon in many fields. Some people resist change tenaciously, even in the face of evidence and at the risk of failure. Another holy grail of empirical software engineering research is finding a way to tell good programmers from bad. A widely quoted piece of folklore says that good programmers are 28 times better than average or 40, or , or some other large number.

Yet everyone who has programmed for a living knows that there really are huge differences in productivity and capability among programmers. Other than hiring someone and watching her work for a couple of years, how can we spot the stars? Figure 4. Jo Erskine Hannay at Simula Research Laboratory has addressed the issues of talent and expertise by breaking the question into parts: Can we actually define what it means to be a good software developer? Can we reliably determine that one developer is better than another?

Hannay zeroes in on a field called individual differences research, in which individuals are classified by characteristics that separate one from another, such as personality.



5 Reasons Why Software Engineers Are in High Demand

Software Engineering and the other "branches" of Computer Science - from Machine Learning to Cybersecurity, Computer Engineering to Human Computer Interaction - all offer high-growth career paths and fascinating academic areas of study. Which pursuit is right for you, based on your unique goals, interests and long-term aspirations? Both pursuits require basic-to-intermediate programming coding skills, but beyond that, do you want to zero in on the immediate, practical skills like programming algorithms without needing the "why", or do you need to know "why" things work the way they do, zooming out to see the bigger picture of low- vs. Do you want the long-term career mobility and versatility a broad Computer Science platform provides, or do you want to focus on building products and services as a Software Engineer or Developer? Software engineering is the process of developing new software to solve problems, help individuals accomplish tasks, and deliver solutions to meet different needs across a range of industries.

Could I finally start calling myself something of a Software Engineer? “Web development isn't real programming”. Not according to the Internet.

Software engineer

Considering a career in software engineering? At a high level, software engineers architect applications for various systems, such as web and mobile. Software engineers write code, design programs, and implement processes to create products and solutions for businesses and government entities. Many software engineers have obtained a degree in computer science or a related field, like electrical engineering, computer engineering, or another programming intensive subject. However, a degree is not always a requirement to get a job in software engineering. Some software engineers are self-taught and some have learned the skills necessary for software engineering through a coding bootcamp. Some large tech companies, like Google , have relaxed their degree requirements for software engineering candidates, instead choosing the focus on the skills and experiences. The great thing about starting a career in software engineering: there are jobs with companies of all sizes and all locations.


Programmer vs. Software Engineer: What’s the Difference?

software engineers are not real engineers

I've worked at various tech companies: from "traditional" shops and consultancies, through an investment bank, to high-growth tech firms. I've also talked with software engineers working at startups, banking, automotive, big tech, and more "traditional" companies. This mix had a healthy sample of Silicon-Valley companies and ones headquartered outside this region. I've noticed that Silicon Valley companies consistently "get" a few things that their traditional counterparts fail to either understand or implement in practice - especially in Europe. These are practices that result in faster innovation at a company-level, better professional growth for engineers, and just better "utilization", for the better word for it.

Software engineering is a broad term.

Are software engineers real engineers?

Please update your browser. You could design, build, deploy and run innovations across our cloud, cyber, digital, markets and payments businesses. Your responsibilities will vary based on your location and team assignment. You'll build innovative solutions that make a difference for our customers, clients, and employees. The Software Engineer Program will allow you to explore agile software development methodologies, pair programming, resiliency patterns and chaos engineering, and more.


The Main Difference Between Software Engineers and IT Support

Well, good news: you can absolutely become a software developer without a college degree! Additionally, the profession offers an above-average work-life balance. Meanwhile, the average growth for all occupations is 4 percent. This translates into , job openings. And guess what? As you can see in the image below , in there were 1.

We spoke to four software engineers about everything from how they launched their careers to I think it's just not something that has really caught on.

Real-Time Software Engineer

As researchers investigate how software gets made, a new empire for empirical research opens up. DOI: Software engineering has long considered itself one of the hard sciences.


The 50 best job boards for software engineers

The MLH Fellowship is a 12 week internship alternative for aspiring software engineers. Fellows on the Software Engineering track experience what it's like to collaborate in a small group on a real project from one of our corporate or government partners. The MLH Fellowship runs year-round with batches starting every few months. We offer scheduling options that accommodate most major time zones. Software Engineering is one of the most in-demand skills that tech companies are hiring for.

I switched to software engineering because I wanted to learn things that were not in other courses.

How Many Software Developers Are in the US and the World? [Updated]

Programmer vs. Getting a better understanding of each role and how they interact can help you determine your path in the industry. As you explore the differences between the two, keep in mind that there will still be a fair amount of overlap when you browse job listings. While this article is intended to highlight their technical differences, employers may not be aware of the intricacies of each title, which can result in the two terms being used interchangeably. Typically, programmers use code to perform their jobs, working closely with engineers, designers, and other programmers, who can provide further instructions and guidance on projects. Keep in mind, coding is a general term that refers simply to writing code. As such, programmers tend to write code with an entire project in mind, while coders take a narrower approach, focusing on a particular part or feature in a larger project.

Thousands of Silicon Valley's tech workers need to stop calling themselves engineers if all they engineer is lines of code, according to a column for The Atlantic. Ian Bogost makes the argument that the term "engineer" is a position that is "regulated, certified and subject to apprenticeship and continuing education. He says 50 years' worth of attempts to turn software development into a legitimate engineering practice have failed.


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  1. Aethelhard

    the Justa phrase

  2. Mosho

    What necessary phrase... super, a brilliant idea

  3. Jeremee

    Can't believe you :)

  4. Kagataxe

    Remarkable, this funny opinion

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