To be a good programmer is difficult and noble. The hardest part of making real a collective vision of a software project is dealing with one's coworkers and customers. Writing computer programs is important and takes great intelligence and skill.
Dedication
To the programmers of Hire.com.
Chapter 1. Introduction
To be a good programmer is difficult and noble. The hardest part of making real a collective vision of a software project is dealing with one's coworkers and customers. Writing computer programs is important and takes great intelligence and skill. But it is really child's play compared to everything else that a good programmer must do to make a software system that succeeds for both the customer and myriad colleagues for whom she is partially responsible. In this essay I attempt to summarize as concisely as possible those things that I wish someone had explained to me when I was twenty-one.
This is very subjective and, therefore, this essay is doomed to be personal and somewhat opinionated. I confine myself to problems that a programmer is very likely to have to face in her work. Many of these problems and their solutions are so general to the human condition that I will probably seem preachy. I hope in spite of this that this essay will be useful.
Computer programming is taught in courses. The excellent books: The Pragmatic Programmer [Prag99], Code Complete [CodeC93], Rapid Development [RDev96], and Extreme Programming Explained [XP99] all teach computer programming and the larger issues of being a good programmer. The essays of Paul Graham[PGSite] and Eric Raymond[Hacker] should certainly be read before or along with this article. This essay differs from those excellent works by emphasizing social problems and comprehensively summarizing the entire set of necessary skills as I see them.
In this essay the term boss to refer to whomever gives you projects to do. I use the words business, company, and tribe, synonymously except that business connotes moneymaking, company connotes the modern workplace and tribe is generally the people you share loyalty with.
Welcome to the tribe.
Chapter 2. Beginner
Personal Skills
Debugging is the cornerstone of being a programmer. The first meaning of the verb to debug is to remove errors, but the meaning that really matters is to see into the execution of a program by examining it. A programmer that cannot debug effectively is blind.
Idealists that think design, or analysis, or complexity theory, or whatnot, are more fundamental are not working programmers. The working programmer does not live in an ideal world. Even if you are perfect, your are surrounded by and must interact with code written by major software companies, organizations like GNU, and your colleagues. Most of this code is imperfect and imperfectly documented. Without the ability to gain visibility into the execution of this code the slightest bump will throw you permanently. Often this visibility can only be gained by experimentation, that is, debugging.
Debugging is about the running of programs, not programs themselves. If you buy something from a major software company, you usually don't get to see the program. But there will still arise places where the code does not conform to the documentation (crashing your entire machine is a common and spectacular example), or where the documentation is mute. More commonly, you create an error, examine the code you wrote and have no clue how the error can be occurring. Inevitably, this means some assumption you are making is not quite correct, or some condition arises that you did not anticipate. Sometimes the magic trick of staring into the source code works. When it doesn't, you must debug.
To get visibility into the execution of a program you must be able to execute the code and observe something about it. Sometimes this is visible, like what is being displayed on a screen, or the delay between two events. In many other cases, it involves things that are not meant to be visible, like the state of some variables inside the code, which lines of code are actually being executed, or whether certain assertions hold across a complicated data structure. These hidden things must be revealed.
The common ways of looking into the ‘innards’ of an executing program can be categorized as:
Using a debugging tool,
Printlining --- Making a temporary modification to the program, typically adding lines that print information out, and
Logging --- Creating a permanent window into the programs execution in the form of a log.
Debugging tools are wonderful when they are stable and available, but the printlining and logging are even more important. Debugging tools often lag behind language development, so at any point in time they may not be available. In addition, because the debugging tool may subtly change the way the program executes it may not always be practical. Finally, there are some kinds of debugging, such as checking an assertion against a large data structure, that require writing code and changing the execution of the program. It is good to know how to use debugging tools when they are stable, but it is critical to be able to employ the other two methods.
Some beginners fear debugging when it requires modifying code. This is understandable---it is a little like exploratory surgery. But you have to learn to poke at the code and make it jump; you have to learn to experiment on it, and understand that nothing that you temporarily do to it will make it worse. If you feel this fear, seek out a mentor---we lose a lot of good programmers at the delicate onset of their learning to this fear.
Debugging is fun, because it begins with a mystery. You think it should do something, but instead it does something else. It is not always quite so simple---any examples I can give will be contrived compared to what sometimes happens in practice. Debugging requires creativity and ingenuity. If there is a single key to debugging is to use the divide and conquer technique on the mystery.
Suppose, for example, you created a program that should do ten things in a sequence. When you run it, it crashes. Since you didn't program it to crash, you now have a mystery. When out look at the output, you see that the first seven things in the sequence were run successfully. The last three are not visible from the output, so now your mystery is smaller: ‘It crashed on thing #8, #9, or #10.’
Can you design an experiment to see which thing it crashed on? Sure. You can use a debugger or we can add printline statements (or the equivalent in whatever language you are working in) after #8 and #9. When we run it again, our mystery will be smaller, such as ‘It crashed on thing #9.’ I find that bearing in mind exactly what the mystery is at any point in time helps keep one focused. When several people are working together under pressure on a problem it is easy to forget what the most important mystery is.
The key to divide and conquer as a debugging technique is the same as it is for algorithm design: as long as you do a good job splitting the mystery in the middle, you won't have to split it too many times, and you will be debugging quickly. But what is the middle of a mystery? There is where true creativity and experience comes in.
To a true beginner, the space of all possible errors looks like every line in the source code. You don't have the vision you will later develop to see the other dimensions of the program, such as the space of executed lines, the data structure, the memory management, the interaction with foreign code, the code that is risky, and the code that is simple. For the experience programmer, these other dimensions form an imperfect but very useful mental model of all the things that can go wrong. Having that mental model is what helps one find the middle of the mystery effectively.
Once you have evenly subdivided the space of all that can go wrong, you must try to decide in which space the error lies. In the simple case where the mystery is: ‘Which single unknown line makes my program crash?’, you can ask yourself: ‘Is the unknown line executed before or after this line that I judge to be executed in the about the middle of the running program?’ Usually you will not be so lucky as to know that the error exists in a single line, or even a single block. Often the mystery will be more like: ‘Either there is a pointer in that graph that points to the wrong node, or my algorithm that adds up the variables in that graph doesn't work.’ In that case you may have to write a small program to check that the pointers in the graph are all correct in order to decide which part of the subdivided mystery can be eliminated.
I've intentionally separated the act of examining a program's execution from the act of fixing an error. But of course, debugging does also mean removing the bug. Ideally you will have perfect understanding of the code and will reach an ‘A-Ha!’ moment where you perfectly see the error and how to fix it. But since your program will often use insufficiently documented systems into which you have no visibility, this is not always possible. In other cases the code is so complicated that your understanding cannot be perfect.
In fixing a bug, you want to make the smallest change that fixes the bug. You may see other things that need improvement; but don't fix those at the same time. Attempt to employ the scientific method of changing one thing and only one thing at a time. The best process for this is to be able to easily reproduce the bug, then put your fix in place, and then rerun the program and observe that the bug no longer exists. Of course, sometimes more than one line must be changed, but you should still conceptually apply a single atomic change to fix the bug.
Sometimes, there are really several bugs that look like one. It is up to you to define the bugs and fix them one at a time. Sometimes it is unclear what the program should do or what the original author intended. In this case, you must exercise your experience and judgment and assign your own meaning to the code. Decide what it should do, and comment it or clarify it in some way and then make the code conform to your meaning. This is an intermediate or advanced skill that is sometimes harder than writing the original function in the first place, but the real world is often messy. You may have to fix a system you cannot rewrite.
Logging is the practice of writing a system so that it produces a sequence of informative records, called a log. Printlining is just producing a simple, usually temporary, log. Absolute beginners must understand and use logs because their knowledge of the programming is limited; system architects must understand and use logs because of the complexity of the system. The amount of information that is provided by the log should be configurable, ideally while the program is running. In general, logs offer three basic advantages:
Logs can provide useful information about bugs that are hard to reproduce (such as those that occur in the production environment but that cannot be reproduced in the test environment).
Logs can provide statistics and data relevant to performance, such as the time passing between statements.
When configurable, logs allow general information to be captured in order to debug unanticipated specific problems without having to modify and/or redeploy the code just to deal with those specific problems.
The amount to output into the log is always a compromise between information and brevity. Too much information makes the log expensive and produces scroll blindness, making it hard to find the information you need. Too little information and it may not contain what you need. For this reason, making what is output configurable is very useful. Typically, each record in the log will identify its position in the source code, the thread that executed it if applicable, the precise time of execution, and, commonly, an additional useful piece of information, such as the value of some variable, the amount of free memory, the number of data objects, etc. These log statements are sprinkled throughout the source code but are particularly at major functionality points and around risky code. Each statement can be assigned a level and will only output a record if the system is currently configured to output that level. You should design the log statements to address problems that you anticipate. Anticipate the need to measure performance.
If you have a permanent log, printlining can now be done in terms of the log records, and some of the debugging statements will probably be permanently added to the logging system.
Learning to understand the performance of a running system is unavoidable for the same reason that learning debugging is. Even if the code you understand perfectly precisely the cost of the code you write, your code will make calls into other software systems that you have little control over or visibility into. However, in practice performance problems are a little different and a little easier than debugging in general.
Suppose that you or your customers consider a system or a subsystem to be too slow. Before you try to make it faster, you must build a mental model of why it is slow. To do this you can use a profiling tool or a good log to figure out where the time or other resources are really being spent. There is a famous dictum that 90% of the time will be spent in 10% of the code. I would add to that the importance of input/output expense (I/O) to performance issues. Often most of the time is spent in I/O in one way or another. Finding the expensive I/O and the expensive 10% of the code is a good first step to building your mental model.
There are many dimensions to the performance of a computer system, and many resources consumed. The first resource to measure is wall--clock time, the total time that passes for the computation. Logging wall-clock time is particularly valuable because it can inform about unpredictable circumstance that arise in situations where other profiling is impractical. However, this may not always represent the whole picture. Sometimes something that takes a little longer but doesn't burn up so many processor seconds will be much better in computing environment you actually have to deal with. Similarly, memory, network bandwidth, database or other server accesses may, in the end, be far more expensive than processor seconds.
Contention for shared resources that are synchronized can cause deadlock and starvation. Deadlock is the inability to proceed because of improper synchronization or resource demands. Starvation is the failure to schedule a component properly. If it can be at all anticipated, it is best to have a way of measuring this contention from the start of your project. Even if this contention does not occur, it is very helpful to be able to assert that with confidence.
Most software projects can be made with relatively little effort 10 to 100 times faster than they are at the they are first released. Under time-to-market pressure, it is both wise and effective to choose a solution that gets the job done simply and quickly, but less efficiently than some other solution. However, performance is a part of usability, and often it must eventually be considered more carefully.
The key to improving the performance of a very complicated system is to analyze it well enough to find the bottlenecks, or places where most of the resources are consumed. There is not much sense in optimizing a function that accounts for only 1% of the computation time. As a rule of thumb you should think carefully before doing anything unless you think it is going to make the system or a significant part of it at least twice as fast. There is usually a way to do this. Consider the test and quality assurance effort that your change will require. Each change brings a test burden with it, so it is much better to have a few big changes.
After you've made a two-fold improvement in something, you need to at least rethink and perhaps reanalyze to discover the next-most-expensive bottleneck in the system, and attack that to get another two-fold improvement.
Often, the bottlenecks in performance will be an example of counting cows by counting legs and dividing by four, instead of counting heads. For example, I've made errors such as failing to provide a relational database system with a proper index on a column I look up a lot, which probably made it at least 20 times slower. Other examples include doing unnecessary I/O in inner loops, leaving in debugging statements that are no longer needed, unnecessary memory allocation, and, in particular, inexpert use of libraries and other subsystems that are often poorly documented with respect to performance. This kind of improvement is sometimes called low-hanging fruit, meaning that it can be easily picked to provide some benefit.
What do you do when you start to run out of low-hanging fruit? Well, you can reach higher, or chop the tree down. You can continue making small improvements or you can seriously redesign a system or a subsystem. (This is a great opportunity to use your skills as a good programmer, not only in the new design but also in convincing your boss that this is a good idea.) However, before you argue for the redesign of a subsystem, you should ask yourself whether or not your proposal will make it five to ten time better.
Sometimes you'll encounter loops, or recursive functions, that take a long time to execute and are bottlenecks in your product. Before you try to make the loop a little faster, but spend a few minutes considering if there is a way to remove it entirely. Would a different algorithm do? Could you compute that while computing something else? If you can't find away around it, then you can optimize the loop. This is simple; move stuff out. In the end, this will require not only ingenuity but also an understanding of the expense of each kind of statement and expression. Here are some suggestions:
Remove floating point operations.
Don't allocate new memory blocks unnecessarily.
Fold constants together.
Move I/O into a buffer.
Try not to divide.
Try not to do expensive typecasts.
Move a pointer rather than recomputing indices.
The cost of each of these operations depends on your specific system. On some systems compilers and hardware do these things for you. Clear, efficient code is better than code that requires an understanding of a particular platform.
For a lot of problems, processors are fast compared to the cost of communicating with a hardware device. This cost is usually abbreviated I/O, and can include network cost, disk I/O, database queries, file I/O, and other use of some hardware not very close to the processor. Therefore building a fast system is often more a question of improving I/O than improving the code in some tight loop, or even improving an algorithm.
There are two very fundamental techniques to improving I/O: caching and representation. Caching is avoiding I/O (generally avoiding the reading of some abstract value) by storing a copy of that value locally so no I/O is performed to get the value. The first key to caching is to make it crystal clear which data is the master and which are copies. There is only one master---period. Caching brings with it the danger that the copy is sometimes can't reflect changes to the master instantaneously.
Representation is the approach of making I/O cheaper by representing data more efficiently. This is often in tension with other demands, like human readability and portability.
Representations can often be improved by a factor of two or three from their first implementation. Techniques for doing this include using a binary representation instead of one that is human readable, transmitting a dictionary of symbols along with the data so that long symbols don't have to be encoded, and, at the extreme, things like Huffman encoding.
A third technique that is sometimes possible is to improve the locality of reference by pushing the computation closer to the data. For instance, if you are reading some data from a database and computing something simple from it, such as a summation, try to get the database server to do it for you. This is highly dependent on the kind of system you're working with, but you should explore it.
Memory is a precious resource that you can't afford to run out of. You can ignore it for a while but eventually you will have to decide how to manage memory.
Space that needs to persist beyond the scope of a single subroutine is often called heap allocated. A chunk of memory is useless, hence garbage, when nothing refers to it. Depending on the system you use, you may have to explicitly deallocate memory yourself when it is about to become garbage. More often you may be able to use a system that provides a garbage collector. A garbage collector notices garbage and frees its space without any action required by the programmer. Garbage collection is wonderful: it lessens errors and increases code brevity and concision cheaply. Use it when you can.
But even with garbage collection, you can fill up all memory with garbage. A classic mistake is to use a hash table as a cache and forget to remove the references in the hash table. Since the reference remains, the referent is noncollectable but useless. This is called a memory leak. You should look for and fix memory leaks early. If you have long running systems memory may never be exhausted in testing but will be exhausted by the user.
The creation of new objects is moderately expensive on any system. Memory allocated directly in the local variables of a subroutine, however, is usually cheap because the policy for freeing it can be very simple. You should avoid unnecessary object creation.
An important case occurs when you can define an upper bound on the number of objects you will need at one time. If these objects all take up the same amount of memory, you may be able to allocate a single block of memory, or a buffer, to hold them all. The objects you need can be allocated and released inside this buffer in a set rotation pattern, so it is sometimes called a ring buffer. This is usually faster than heap allocation.
Sometimes you have to explicitly free allocated space so it can be reallocated rather than rely on garbage collection. Then you must apply careful intelligence to each chunk of allocated memory and design a way for it to be deallocated at the appropriate time. The method may differ for each kind of object you create. You must make sure that every execution of a memory allocating operation is matched by a memory deallocating operation eventually. This is so difficult that programmers often simply implement a rudimentary form or garbage collection, such as reference counting, to do this for them.
The intermittent bug is a cousin of the 50-foot-invisible-scorpion-from-outer-space kind of bug. This nightmare occurs so rarely that it is hard to observe, yet often enough that it can't be ignored. You can't debug because you can't find it.
Although after 8 hours you will start to doubt it, the intermittent bug has to obey the same laws of logic everything else does. What makes it hard is that it occurs only under unknown conditions. Try to record the circumstances under which the bug does occur, so that you can guess at what the variability really is. The condition may be related to data values, such as ‘This only happens when we enter Wyoming as a value.’ If that is not the source of variability, the next suspect should be improperly synchronized concurrency.
Try, try, try to reproduce the bug in a controlled way. If you can't reproduce it, set a trap for it by building a logging system, a special one if you have to, that can log what you guess you need when it really does occur. Resign yourself to that if the bug only occurs in production and not at your whim, this is may be a long process. The hints that you get from the log may not provide the solution but may give you enough information to improve the logging. The improved logging system may take a long time to be put into production. Then, you have to wait for the bug to reoccur to get more information. This cycle can go on for some time.
The stupidest intermittent bug I ever created was in a multi-threaded implementation of a functional programming language for a class project. I had very carefully insured correct concurrent evaluation of the functional program, good utilization of all the CPUs available (eight, in this case). I simply forgot to synchronize the garbage collector. The system could run a long time, often finishing whatever task I began, before anything noticeable went wrong. I'm ashamed to admit I had begun to question the hardware before my mistake dawned on me.
At work we recently had an intermittent bug that took us several weeks to find. We have multi-threaded application servers in Java™ behind Apache™ web servers. To maintain fast page turns, we do all I/O in small set of four separate threads that are different than the page-turning threads. Every once in a while these would apparently get ‘stuck’ and cease doing anything useful, so far as our logging allowed us to tell, for hours. Since we had four threads, this was not in itself a giant problem---unless all four got stuck. Then the queues emptied by these threads would quickly fill up all available memory and crash our server. It took us about a week to figure this much out, and we still didn't know what caused it, when it would happen, or even what the threads where doing when they got ‘stuck’.
This illustrates some risk associated with third-party software. We were using a licensed piece of code that removed HTML tags from text. Due to its place of origin we affectionately referred to this as ‘the French stripper.’ Although we had the source code (thank goodness!) we had not studied it carefully until by turning up the logging on our servers we finally realized that the email threads were getting stuck in the French stripper.
The stripper performed well except on some long and unusual kinds of texts. On these texts, the code was quadratic or worse. This means that the processing time was proportional to the square of the length of the text. Had these texts occurred commonly, we would have found the bug right away. If they had never occurred at all, we would never have had a problem. As it happens, it took us weeks to finally understand and resolve the problem.
To learn how to design software, study the action of a mentor by being physically present when they are designing. Then study well-written pieces of software. After that, you can read some books on the latest design techniques.
Then you must do it yourself. Start with a small project. When you are finally done, consider how the design failed or succeeded and how you diverged from your original conception. They move on to larger projects, hopefully in conjunction with other people. Design is a matter of judgment that takes years to acquire. A smart programmer can learn the basics adequately in two months and can improve from there.
It is natural and helpful to develop your own style, but remember that design is an art, not a science. People who write books on the subject have a vested interest in making it seem scientific. Don't become dogmatic about particular design styles.
The late, great Edsger Dijkstra has eloquently explained that Computer Science is not an experimental science[ExpCS] and doesn't depend on electronic computers. As he puts it referring to the 1960s[Knife],
...the harm was done: the topic became known as “computer science”---which, actually, is like referring to surgery as “knife science” --- and it was firmly implanted in people's minds that computing science is about machines and their peripheral equipment.
Programming ought not to be an experimental science, but most working programmers do not have the luxury of engaging in what Dijkstra means by computing science. We must work in the realm of experimentation, just as some, but not all, physicists do. If thirty years from now programming can be performed without experimentation, it will be a great accomplishment of Computer Science.
The kinds of experiments you will have to perform include:
Testing systems with small examples to verify that they conform to the documentation or to understand their response when there is no documentation,
Testing small code changes to see if they actually fix a bug,
Measuring the performance of a system under two different conditions due to imperfect knowledge of there performance characteristics,
Checking the integrity of data, and
Collecting statistics that may hint at the solution to difficult or hard-to-repeat bugs.
I don't think in this essay I can explain the design of experiments; you will have to study and practice. However, I can offer two bits of advice.
First, try to be very clear about your hypothesis, or the assertion that you are trying to test. It also helps to write the hypothesis down, especially if you find yourself confused or are working with others.
You will often find yourself having to design a series of experiments, each of which is based on the knowledge gained from the last experiment. Therefore, you should design your experiments to provide the most information possible. Unfortunately, this is in tension with keeping each experiment simple---you will have to develop this judgment through experience.
To get a working software system in active use as quickly as possible requires not only planning the development, but also planning the documentation, deployment, marketing. In a commercial project it also requires sales and finance. Without predictability of the development time, it is impossible to plan these effectively.
Good estimation provides predictability. Managers love it, as well they should. The fact that it is impossible, both theoretically and practically, to predict accurately how long it will take to develop software is often lost on managers. We are asked to do this impossible thing all the time, and we must face up to it honestly. However, it would be dishonest not to admit the impossibility of this task, and when necessary, explain it. There is a lot of room for miscommunication about estimates, as people have a startling tendency to think wishfully that the sentence:
I estimate that, if I really understand the problem, it is about 50% likely that we will be done in five weeks (if no one bothers us during that time).
really means:
I promise to have it all done five weeks from now.
This common interpretation problem requires that you explicitly discuss what the estimate means with your boss or customer as if they were a simpleton. Restate your assumptions, no matter how obvious they seem to you.
Estimation takes practice. It also takes labor. It takes so much labor it may be a good idea to estimate the time it will take to make the estimate, especially if you are asked to estimate something big.
When asked to provide an estimate of something big, the most honest thing to do is to stall. Most engineers are enthusiastic and eager to please, and stalling certainly will displease the stalled. But an on-the-spot estimate probably won't be accurate and honest.
While stalling, it may be possible to consider doing or prototyping the task. If political pressure permits, this is the most accurate way of producing the estimate, and it makes real progress.
When not possible to take the time for some investigation, you should first establish the meaning of the estimate very clearly. Restate that meaning as the first and last part of your written estimate. Prepare a written estimate by deconstructing the task into progressively smaller subtasks until each small task is no more than a day; ideally at most in length. The most important thing is not to leave anything out. For instance, documentation, testing, time for planning, time for communicating with other groups, and vacation time are all very important. If you spend part of each day dealing with knuckleheads, put a line item for that in the estimate. This gives your boss visibility into what is using up your time at a minimum, and might get you more time.
I know good engineers who pad estimates implicitly, but I recommend that you do not. One of the results of padding is trust in you may be depleted. For instance, an engineer might estimate three days for a task that she truly thinks will take one day. The engineer may plan to spend two days documenting it, or two days working on some other useful project. But it will be detectable that the task was done in only one day (if it turns out that way), and the appearance of slacking or overestimating is born. It's far better to give proper visibility into what you are actually doing. If documentation takes twice as long as coding and the estimate says so, tremendous advantage is gained by making this visible to the manager.
Pad explicitly instead. If a task will probably take one day---but might take ten days if your approach doesn't work---note this somehow in the estimate if you can; if not, at least do an average weighted by your estimates of the probabilities. Any risk factor that you can identify and assign an estimate to should go into the schedule. One person is unlikely to be sick in any given week. But a large project with many engineers will have some sick time; likewise vacation time. And what is the probability of a mandatory company-wide training seminar? If it can be estimated, stick it in. There are of course, unknown unknowns, or unk-unks. Unk-unks by definition cannot be estimated individually. You can try to create a global line item for all unk-unks, or handle them in some other way that you communicate to your boss. You cannot, however, let your boss forget that they exist, and it is devilishly easy for an estimate to become a schedule without the unk-unks considered.
In a team environment, you should try to have the people who will do the work do the estimate, and you should try to have team-wide consensus on estimates. People vary widely in skill, experience, preparedness, and confidence. Calamity strikes when a strong programmer estimates for herself and then weak programmers are held to this estimate. The act of having the whole team agree on a line-by-line basis to the estimate clarifies the team understanding, as well as allowing the opportunity for tactical reassignment of resources (for instance, shifting burden away from weaker team members to stronger).
If there are big risks that cannot be evaluated, it is your duty to state so forcefully enough that your manager does not commit to them and then become embarrassed when the risk occurs. Hopefully in such a case whatever is needed will be done to decrease the risk.
If you can convince your company to use Extreme Programming, you will only have to estimate relatively small things, and this is both more fun and more productive.
The nature of what you need to know determines how you should find it.
If you need information about concrete things that are objective and easy to verify, for example the latest patch level of a software product, ask a large number of people politely by searching the internet for it or by posting on a discussion group. Don't search on the internet for anything that smacks of either opinion or subjective interpretation: the ratio of drivel to truth is too high.
If you need general knowledge about something subjective the history of what people have thought about it, go to the library (the physical building in which books are stored). For example, to learn about math or mushrooms or mysticism, go to the library.
If you need to know how to do something that is not trivial get two or three books on the subject and read them. You might learn how to do something trivial, like install a software package, from the Internet. You can even learn important things, like good programming technique, but you can easily spend more time searching and sorting the results and attempting to divine the authority of the results than it would take to read the pertinent part of a solid book.
If you need information that no one else could be expected to know for example, ‘does this software that is brand new work on gigantic data sets?’, you must still search the internet and the library. After those options are completely exhausted, you may design an experiment to ascertain it.
If you want an opinion or a value judgment that takes into account some unique circumstance, talk to an expert. For instance, if you want to know whether or not it is a good idea to build a modern database management system in LISP, you should talk to a LISP expert and a database expert.
If you want to know how likely it is that a faster algorithm for a particular application exists that has not yet been published, talk to someone working in that field.
If you want to make a personal decision that only you can make like whether or not you should start a business, try putting into writing a list of arguments for and against the idea. If that fails, consider divination. Suppose you have studied the idea from all angles, have done all your homework, and worked out all the consequences and pros and cons in your mind, and yet still remain indecisive. You now must follow your heart and tell your brain to shut up. The multitude of available divination techniques are very useful for determining your own semi-conscious desires, as they each present a complete ambiguous and random pattern that your own subconscious will assign meaning to.
Respect every person's time and balance it against your own. Asking someone a question accomplishes far more than just receiving the answer. The person learns about you, both by enjoying your presence and hearing the particular question. You learn about the person in the same way, and you may learn the answer you seek. This is usually far more important than your question.
However, the value of this diminishes the more you do it. You are, after all, using the most precious commodity a person has: their time. The benefits of communication must be weighed against the costs. Furthermore, the particular costs and benefits derived differ from person to person. I strongly believe that an executive of 100 people should spend five minutes a month talking to each person in her organization, which would be about 5% of their time. But ten minutes might be too much, and five minutes is too much if they have one thousand employees. The amount of time you spend talking to each person in your organization depends on their role (more than their position). You should talk to your boss more than your boss's boss, but you should talk to your boss's boss a little. It may be uncomfortable, but I believe you have a duty to talk a little bit to all your superiors, each month, no matter what.
The basic rule is that everyone benefits from talking to you a little bit, and the more they talk to you, the less benefit they derive. It is your job to provide them this benefit, and to get the benefit of communicating with them, keeping the benefit in balance with the time spent.
It is important to respect your own time. If talking to someone, even if it will cost them time, will save you a great deal of time, then you should do it unless you think their time is more valuable than yours, to the tribe, by that factor.
A strange example of this is the summer intern. A summer intern in a highly technical position can't be expected to accomplish too much; they can be expected to pester the hell out of everybody there. So why is this tolerated? Because the pestered are receiving something important from the intern. They get a chance to showoff a little. They get a chance to hear some new ideas, maybe; they get a chance to see things from a different perspective. They may also be trying to recruit the intern, but even if this is not the case there is much to gain.
You should ask people for a little bit of their wisdom and judgment whenever you honestly believe they have something to say. This flatters them and you will learn something and teach them something. A good programmer does not often need the advice of a Vice President of Sales, but if you ever do, you be sure to ask for it. I once asked to listen in on a few sales calls to better understand the job of our sales staff. This took no more than 30 minutes but I think that small effort made an impression on the sales force.
Life is too short to write crap nobody will read; if you write crap, nobody will read it. Therefore a little good documentation is best. Managers often don't understand this, because even bad documentation gives them a false sense of security that they are not dependent on their programmers. If someone absolutely insists that you write truly useless documentation, say ``yes'' and quietly begin looking for a better job.
There's nothing quite as effective as putting an accurate estimate of the amount of time it will take to produce good documentation into an estimate to slacken the demand for documentation. The truth is cold and hard: documentation, like testing, can take many times longer than developing code.
Writing good documentation is, first of all, good writing. I suggest you find books on writing, study them, and practice. But even if you are a lousy writer or have poor command of the language in which you must document, the Golden Rule is all you really need: ``Do unto others as you would have them do unto you.'' Take time to really think about who will be reading your documentation, what they need to get out of it, and how you can teach that to them. If you do that, you will be an above average documentation writer, and a good programmer.
When it comes to actually documenting code itself, as opposed to producing documents that can actually be read by non-programmers, the best programmers I've ever known hold a universal sentiment: write self-explanatory code and only document code in the places that you cannot make it clear by writing the code itself. There are two good reasons for this. First, anyone who needs to see code-level documentation will in most cases be able to and prefer to read the code anyway. Admittedly, this seems easier to the experienced programmer than to the beginner. More importantly however, is that the code and the documentation cannot be inconsistent if there is no documentation. The source code can at worst be wrong and confusing. The documentation, if not written perfectly, can lie, and that is a thousand times worse.
This does not make it easier on the responsible programmer. How does one write self-explanatory code? What does that even mean? It means:
Writing code knowing that someone will have to read it;
Applying the golden rule;
Choosing a solution that is straightforward, even if you could get by with another solution faster;
Sacrificing small optimizations that obfuscate the code;
Thinking about the reader and spending some of your precious time to make it easier on her; and
Not ever using a function name like ``foo'',``bar'', or ``doIt''!
It is very common to have to work with poor quality code that someone else has written. Don't think too poorly of them, however, until you have walked in their shoes. They may have been asked very consciously to get something done quickly to meet schedule pressure. Regardless, in order to work with unclear code you must understand it. To understand it takes learning time, and that time will have to come out of some schedule, somewhere, and you must insist on it. To understand it, you will have to read the source code. You will probably have to experiment with it.
This is a good time to document, even if it is only for yourself, because the act of trying to document the code will force you to consider angles you might not have considered, and the resulting document may be useful. While you're doing this, consider what it would take to rewrite some or all of the code. Would it actually save time to rewrite some of it? Could you trust it better if you rewrote it? Be careful of arrogance here. If you rewrite it, it will be easier for you to deal with, but will it really be easier for the next person who has to read it? If you rewrite it, what will the test burden be? Will the need to re-test it outweigh any benefits that might be gained?
In any estimate that you make for work against code you didn't write, the quality of that code should affect your perception of the risk of problems and unk-unks.
It is important to remember that abstraction and encapsulation, two of a programmer's best tools, are particularly applicable to lousy code. You may not be able to redesign a large block of code, but if you can add a certain amount of abstraction to it you can obtain some of the benefits of a good design without reworking the whole mess. In particular, you can try to wall off the parts that are particularly bad so that they may be redesigned independently.
Source code control systems let you manage projects effectively. They're very useful for one person and essential for a group. They track all changes in different versions so that no code is ever lost and meaning can be assigned to changes. One can create throw-away and debugging code with confidence with a source code control system, since the code you modify is kept carefully separate from committed, official code that will be shared with the team or released.
I was late to appreciate the benefits of source code control systems but now I wouldn't live without one even on a one-person project. Generally they are necessary when you have team working on the same code base. However, they have another great advantage: they encourage thinking about the code as a growing, organic system. Since each change is marked as a new revision with a new name or number, one begins to think of the software as a visibly progressive series of improvements. I think this is especially useful for beginners.
A good technique for using a source code control system is to stay within a few days of being up-to-date at all time. Code that can't be finished in a few days is checked in, but in a way that it is inactive and will not be called, and therefore not create any problems for anybody else. Committing a mistake that slows down your teammates is a serious error; it is often taboo.
Unit testing, the testing of an individual piece of coded functionality by the team that wrote it, is a part of coding, not something different from it. Part of designing the code is designing how it will be tested. You should write down a test plan, even if it is only one sentence. Sometimes the test will be simple: ``Does the button look good?'' Sometimes it will be complex: ``Did this matching algorithm return precisely the correct matches?''
Use assertion checking and test drivers whenever possible. This not only catches bugs early, but is very useful later on and lets you eliminate mysteries that you would otherwise have to worry about.
The Extreme Programming developers are writing extensively on unit testing effectively; I can do no better than to recommend their writings.
When stumped, take a break. I sometimes meditate for 15 minutes when stumped and the problem magically unravels when I come back to it. A night's sleep sometimes does the same thing on a larger scale. It's possible that temporarily switching to any other activity may work.
Computer programming is an activity that is also a culture. The unfortunate fact is that it is not a culture that values mental or physical health very much. For both cultural/historical reasons (the need to work at night on unloaded computers, for example) and because of overwhelming time-to-market pressure and the scarcity of programmers, computer programmers are traditionally overworked. I don't think you can trust all the stories you hear, but I think 60 hours a week is common, and 50 is pretty much a minimum. This means that often much more than that is required. This is serious problem for a good programmer, who is responsible not only for themselves but their teammates as well. You have to recognize when to go home, and sometimes when to suggest that other people go home. There can't be any fixed rules for solving this problem, anymore than there can be fixed rules for raising a child, for the same reason---every human being is different.
Beyond 60 hours a week is an extraordinary effort for me, which I can apply for short periods of time (about one week), and that is sometimes expected of me. I don't know if it is fair to expect 60 hours of work from a person; I don't even know if 40 is fair. I am sure, however, that it is stupid to work so much that you are getting little out of that extra hour you work. For me personally, that's any more than 60 hours a week. I personally think a programmer should exercise noblesse oblige and shoulder a heavy burden. However, it is not a programmer's duty to be a patsy. The sad fact is programmers are often asked to be patsies in order to put on a show for somebody, for example a manager trying to impress an executive. Programmers often succumb to this because they are eager to please and not very good at saying no. There are four defenses against this:
Communicate as much as possible with everyone in the company so that no one can mislead the executives about what is going on,
Learn to estimate and schedule defensively and explicitly and give everyone visibility into what the schedule is and where it stands,
Learn to say no, and say no as a team when necessary, and
Quit if you have to.
Most programmers are good programmers, and good programmers want to get a lot done. To do that, they have to manage their time effectively. There is a certain amount of mental inertia associated with getting warmed-up to a problem and deeply involved in it. Many programmers find they work best when they have long, uninterrupted blocks of time in which to get warmed-up and concentrate. However, people must sleep and perform other duties. Each person needs to find a way to satisfy both their human rhythm and their work rhythm. Each programmer needs to do whatever it takes to procure efficient work periods, such as reserving certain days in which you will attend only the most critical meetings.
Since I have children, I try to spend evenings with them sometimes. The rhythm that works best for me is to work a very long day, sleep in the office or near the office (I have a long commute from home to work) then go home early enough the next day to spend time with my children before they go to bed. I am not comfortable with this, but it is the best compromise I have been able to work out. Go home if you have a contagious disease. You should go home if you are thinking suicidal thoughts. You should take a break or go home if you think homicidal thoughts for more than a few seconds. You should send someone home if they show serious mental malfunctioning or signs of mental illness beyond mild depression. If you are tempted to be dishonest or deceptive in a way that you normally are not due to fatigue, you should take a break. Don't use cocaine or amphetamines to combat fatigue. Don't abuse caffeine.
You will probably have to deal with difficult people. You may even be a difficult person yourself. If you are the kind of person who has a lot of conflicts with coworkers and authority figures, you should cherish the independence this implies, but work on your interpersonal skills without sacrificing your intelligence or principles.
This can be very disturbing to some programmers who have no experience in this sort of thing and whose previous life experience has taught them patterns of behavior that are not useful in the workplace. Difficult people are often inured to disagreement and they are less affected by social pressure to compromise than others. The key is to respect them appropriately, which is more than you will want to but not as much as they might want.
Programmers have to work together as a team. When disagreement arises, it must be resolved somehow, it cannot be ducked for long. Difficult people are often extremely intelligent and have something very useful to say. It is critical that you listen and understand the difficult person without prejudice caused by the person. A failure to communicate is often the basis of disagreement but it can sometimes be removed with great patience. Try to keep this communication cool and cordial, and don't accept any baits for greater conflict that may be offered. After a reasonable period of trying to understand, make a decision.
Don't let a bully force you to do something you don't agree with. If you are the leader, do what you think is best. Don't make a decision for any personal reasons, and be prepared to explain the reasons for your decision. If you are a teammate with a difficult person, don't let the leader's decision have any personal impact. If it doesn't go your way, do it the other way whole-heartedly.
Difficult people do change and improve. I've seen it with my own eyes, but it is very rare. However, everyone has transitory ups and downs.
One of the challenges that every programmer but especially leaders face is keeping the difficult person fully engaged. They are more prone to duck work and resist passively than others.
This work is licensed under the GNU Free Documentation License
The original version of this document was written by Robert L. Read without renumeration and dedicated to the programmers of Hire.com.
Author: Robert L