The original article was posted by Michael Veksler on Quora

A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.

The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.

Readability is a tricky thing, and involves several aspects:

1. Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
2. Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
3. Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
4. Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
5. Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
6. Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
7. As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
8. The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.

Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.

Don’t blindly use C++ standard library without understanding what it does - learn it. You look at std::vector::push_back() documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::map, and with std::unordered_map. Knowing the difference between these two maps, you’d know when to use each one of them.

Never call new or delete directly, use std::make_unique and [cost c++]std::make_shared[/code] instead. Try to implement usique_ptr, shared_ptr, weak_ptr yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.

Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.

Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.

Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.

Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.

These are the most important things, which will make you a better programmer. The other things will follow.

Disruptive technologies such as hand-held devices, cloud computing and social media are rattling the foundations upon which traditional businesses are built. Enterprise customers have grown smarter at ensuring the latest technological trends work in their favor. Everyone is trying to zero in on their core competencies by employing commodity services to run their business.

Likewise, enterprise application vendors need to zero in on their core competencies and enhance more value to the businesses of their clientele by leveraging standards-based commodity services, such as IaaS and PaaS, provided by leaders in those segments (e.g. Amazon EC2, Google Cloud Platform etc.).

What else enterprises need to do is learn to adopt new and emerging technologies such as cloud, utility and social computing to build on them to penetrate new market avenues.

New small and medium-sized entrants into the market are constantly challenging enterprises given their ability to rapidly turnaround and address the requirements of the customers in a cost-effective manner. Additionally, these new advancements also affect how enterprises create, deploy, and manage solutions and applications. If you take the example of Force.com, for instance, you find that it’s a common war zone for enterprise application vendors to furnish SME markets with their applications, with the new entrants mostly having an edge.

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?

In recent decades, companies have become remarkably different than what they were in the past. The formal hierarchies through which support staff rose towards management positions are largely extinct. Offices are flat and open-plan collaborations between individuals with varying talent who may not ever physically occupy a corporate workspace. Many employed by companies today work from laptops nomadically instead. No one could complain that IT innovation hasn’t been profitable. It’s an industry that is forecasted to rake in $351 billion in 2018, according to recent statistics from the Consumer Technology Association (CTA). A leadership dilemma for mid-level IT managers in particular, however, has developed. Being in the middle has always been a professional gray area that only the most driven leverage towards successful outcomes for themselves professionally, but mid-level managers in IT need to develop key skills in order to drive the level of growth that the fast paced companies who employ them need. 

What is a middle manager’s role exactly? 

A typical middle manager in the IT industry is usually someone who has risen up the ranks from a technical related position due to their ability to envision a big picture of what’s required to drive projects forward. A successful middle manager is able to create cohesion across different areas of the company so that projects can be successfully completed. They’re also someone with the focus necessary to track the progress of complex processes and drive them forward at a fast pace as well as ensure that outcomes meet or exceed expectations.

What challenges do middle managers face in being successful in the IT industry today? 

While middle managers are responsible for the teams they oversee to reach key milestones in the life cycle of important projects, they struggle to assert their power to influence closure. Navigating the space between higher-ups and atomized work forces is no easy thing, especially now that workforces often consist of freelancers with unprecedented independence. 

What are the skills most needed for an IT manager to be effective? 

Being educated on a steady basis to handle the constant evolution of tech is absolutely essential if a middle manager expects to thrive professionally in a culture so knowledge oriented that evolves at such a rapid pace. A middle manager who doesn't talk the talk of support roles or understand the nuts and bolts of a project they’re in charge of reaching completion will not be able to catch errors or suggest adequate solutions when needed. 

How has the concept of middle management changed? 

Middle managers were basically once perceived of as supervisors who motivated and rewarded staff towards meeting goals. They coached. They toggled back and forth between the teams they watched over and upper management in an effort to keep everyone on the same page. It could be said that many got stuck between the lower and upper tier of their companies in doing so. While companies have always had to be result-oriented to be profitable, there’s a much higher expectation for what that means in the IT industry. Future mid-level managers will have to have the same skills as those whose performance they're tracking so they can determine if projects are being executed effectively. They also need to be able to know what new hires that are being on-boarded should know to get up to speed quickly, and that’s just a thumbnail sketch because IT companies are driven forward by skills that are not easy to master and demand constant rejuvenation in the form of education and training. It’s absolutely necessary for those responsible for teams that bring products and services to market to have similar skills in order to truly determine if they’re being deployed well. There’s a growing call for mid-level managers to receive more comprehensive leadership training as well, however. There’s a perception that upper and lower level managers have traditionally been given more attention than managers in the middle. Some say that better prepped middle managers make more valuable successors to higher management roles. That would be a great happy ending, but a growing number of companies in India’s tech sector complain that mid-level managers have lost their relevance in the scheme of the brave new world of IT and may soon be obsolete.