Week 2 [Fri, Jan 14th] - Topics

The following description of the Joys of the Programming Craft was taken from Chapter 1 of the famous book The Mythical Man-Month, by Frederick P. Brooks.

Why is programming fun? What delights may its practitioner expect as his reward?

First is the sheer joy of making things. As the child delights in his mud pie, so the adult enjoys building things, especially things of his own design. I think this delight must be an image of God's delight in making things, a delight shown in the distinctness and newness of each leaf and each snowflake.

Second is the pleasure of making things that are useful to other people. Deep within, you want others to use your work and to find it helpful. In this respect the programming system is not essentially different from the child's first clay pencil holder "for Daddy's office."

Third is the fascination of fashioning complex puzzle-like objects of interlocking moving parts and watching them work in subtle cycles, playing out the consequences of principles built in from the beginning. The programmed computer has all the fascination of the pinball machine or the jukebox mechanism, carried to the ultimate.

Fourth is the joy of always learning, which springs from the nonrepeating nature of the task. In one way or another the problem is ever new, and its solver learns something: sometimes practical, sometimes theoretical, and sometimes both.

Finally, there is the delight of working in such a tractable medium. The programmer, like the poet, works only slightly removed from pure thought-stuff. He builds his castles in the air, from air, creating by the exertion of the imagination. Few media of creation are so flexible, so easy to polish and rework, so readily capable of realizing grand conceptual structures....

Yet the program construct, unlike the poet's words, is real in the sense that it moves and works, producing visible outputs separate from the construct itself. It prints results, draws pictures, produces sounds, moves arms. The magic of myth and legend has come true in our time. One types the correct incantation on a keyboard, and a display screen comes to life, showing things that never were nor could be.

Programming then is fun because it gratifies creative longings built deep within us and delights sensibilities you have in common with all men.

Not all is delight, however, and knowing the inherent woes makes it easier to bear them when they appear.

First, one must perform perfectly. The computer resembles the magic of legend in this respect, too. If one character, one pause, of the incantation is not strictly in proper form, the magic doesn't work. Human beings are not accustomed to being perfect, and few areas of human activity demand it. Adjusting to the requirement for perfection is, I think, the most difficult part of learning to program.

Next, other people set one's objectives, provide one's resources, and furnish one's information. One rarely controls the circumstances of his work, or even its goal. In management terms, one's authority is not sufficient for his responsibility. It seems that in all fields, however, the jobs where things get done never have formal authority commensurate with responsibility. In practice, actual (as opposed to formal) authority is acquired from the very momentum of accomplishment.

The dependence upon others has a particular case that is especially painful for the system programmer. He depends upon other people's programs. These are often maldesigned, poorly implemented, incompletely delivered (no source code or test cases), and poorly documented. So he must spend hours studying and fixing things that in an ideal world would be complete, available, and usable.

The next woe is that designing grand concepts is fun; finding nitty little bugs is just work. With any creative activity come dreary hours of tedious, painstaking labor, and programming is no exception.

Next, one finds that debugging has a linear convergence, or worse, where one somehow expects a quadratic sort of approach to the end. So testing drags on and on, the last difficult bugs taking more time to find than the first.

The last woe, and sometimes the last straw, is that the product over which one has labored so long appears to be obsolete upon (or before) completion. Already colleagues and competitors are in hot pursuit of new and better ideas. Already the displacement of one's thought-child is not only conceived, but scheduled.

This always seems worse than it really is. The new and better product is generally not available when one completes his own; it is only talked about. It, too, will require months of development. The real tiger is never a match for the paper one, unless actual use is wanted. Then the virtues of reality have a satisfaction all their own.

Of course the technological base on which one builds is always advancing. As soon as one freezes a design, it becomes obsolete in terms of its concepts. But implementation of real products demands phasing and quantizing. The obsolescence of an implementation must be measured against other existing implementations, not against unrealized concepts. The challenge and the mission are to find real solutions to real problems on actual schedules with available resources.

This then is programming, both a tar pit in which many efforts have floundered and a creative activity with joys and woes all its own. For many, the joys far outweigh the woes....

Software development goes through different stages such as requirements, analysis, design, implementation and testing. These stages are collectively known as the software development life cycle (SDLC). There are several approaches, known as software development life cycle models (also called software process models), that describe different ways to go through the SDLC. Each process model prescribes a "roadmap" for the software developers to manage the development effort. The roadmap describes the aims of the development stage(s), the artifacts or outcome of each stage, as well as the workflow i.e. the relationship between stages.

The sequential model, also called the waterfall model, models software development as a linear process, in which the project is seen as progressing steadily in one direction through the development stages. The name waterfall stems from how the model is drawn to look like a waterfall (see below).

When one stage of the process is completed, it should produce some artifacts to be used in the next stage. For example, upon completion of the requirements stage, a comprehensive list of requirements is produced that will see no further modifications. A strict application of the sequential model would require each stage to be completed before starting the next.

This could be a useful model when the problem statement is well-understood and stable. In such cases, using the sequential model should result in a timely and systematic development effort, provided that all goes well. As each stage has a well-defined outcome, the progress of the project can be tracked with relative ease.

The major problem with this model is that the requirements of a real-world project are rarely well-understood at the beginning and keep changing over time. One reason for this is that users are generally not aware of how a software application can be used without prior experience in using a similar application.

The iterative model (sometimes called iterative and incremental) advocates having several iterations of SDLC. Each of the iterations could potentially go through all the development stages, from requirements gathering to testing & deployment. Roughly, it appears to be similar to several cycles of the sequential model.

In this model, each of the iterations produces a new version of the product. Feedback on the new version can then be fed to the next iteration. Taking the Minesweeper game as an example, the iterative model will deliver a fully playable version from the early iterations. However, the first iteration will have primitive functionality, for example, a clumsy text based UI, fixed board size, limited randomization, etc. These functionalities will then be improved in later releases.

The iterative model can take a breadth-first or a depth-first approach to iteration planning.

• breadth-first: an iteration evolves all major components in parallel e.g., add a new feature fully, or enhance an existing feature.
• depth-first: an iteration focuses on fleshing out only some components e.g., update the backend to support a new feature that will be added in a future iteration.

Most projects use a mixture of breadth-first and depth-first iterations i.e., an iteration can contain some breadth-first work as well as some depth-first work.

Revision control software are also known as Version Control Software (VCS), and by a few other names.

Let's take your first few steps in your Git (with GitHub) journey.

1. The first step is to install SourceTree, which is Git + a GUI for Git. If you prefer to use Git via the command line (i.e., without a GUI), you can install Git instead.

2. Next, initialize a repository. Let us assume you want to version control content in a specific directory. In that case, you need to initialize a Git repository in that directory. Here are the steps:

Create a directory for the repo (e.g., a directory named things).

SourceTree

Windows: Click File → Clone/New…. Click on Create button.
Mac: New... → Create New Repository.

Enter the location of the directory (Windows version shown below) and click Create.

Go to the things folder and observe how a hidden folder .git has been created.

Windows: you might have to configure Windows Explorer to show hidden files.

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CLI

Open a Git Bash Terminal.

If you installed SourceTree, you can click the Terminal button to open a GitBash terminal.

Navigate to the things directory.

Use the command git init which should initialize the repo.

$ git init
Initialized empty Git repository in c:/repos/things/.git/

You can use the command ls -a to view all files, which should show the .git directory that was created by the previous command.

$ ls -a
.  ..  .git

You can also use the git status command to check the status of the newly-created repo. It should respond with something like the following:

git status

# On branch master
#
# Initial commit
#
nothing to commit (create/copy files and use "git add" to track)

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Tracking and ignoring

In a repo, you can specify which files to track and which files to ignore. Some files such as temporary log files created during the build/test process should not be revision-controlled.

Staging and committing

After initializing a repository, Git can help you with revision controlling files inside the working directory. However, it is not automatic. It is up to you to tell Git which of your changes (aka revisions) should be committed to its memory for later use. Saving changes into Git's memory in that way is often called committing and a change saved to the revision history is called a commit.

Here are the steps you can follow to learn how to work with Git commits:

1. Do some changes to the content inside the working directory e.g., create a file named fruits.txt in the things directory and add some dummy text to it.

2. Observe how the file is detected by Git.

SourceTree

The file is shown as ‘unstaged’.

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CLI

You can use the git status command to check the status of the working directory.

git status

# On branch master
#
# Initial commit
#
# Untracked files:
#   (use "git add <file>..." to include in what will be committed)
#
#   a.txt
nothing added to commit but untracked files present (use "git add" to track)

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3. Stage the changes to commit: Although Git has detected the file in the working directory, it will not do anything with the file unless you tell it to. Suppose you want to commit the current changes to the file. First, you should stage the file.

SourceTree

Select the fruits.txt and click on the Stage Selected button.

fruits.txt should appear in the Staged files panel now.

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CLI

You can use the stage or the add command (they are synonyms, add is the more popular choice) to stage files.

git add fruits.txt
git status

# On branch master
#
# Initial commit
#
# Changes to be committed:
#   (use "git rm --cached <file>..." to unstage)
#
#       new file:   fruits.txt
#

---

4. Commit the staged version of fruits.txt.

SourceTree

Click the Commit button, enter a commit message e.g. add fruits.txt into the text box, and click Commit.

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CLI

Use the commit command to commit. The -m switch is used to specify the commit message.

git commit -m "Add fruits.txt"

You can use the log command to see the commit history.

git log

commit 8fd30a6910efb28bb258cd01be93e481caeab846
Author: … < … @... >
Date:   Wed Jul 5 16:06:28 2017 +0800

  Add fruits.txt

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Note the existence of something called the master branch. Git allows you to have multiple branches (i.e. it is a way to evolve the content in parallel) and Git auto-creates a branch named master on which the commits go on by default.

5. Do a few more commits.

1. Make some changes to fruits.txt (e.g. add some text and delete some text). Stage the changes, and commit the changes using the same steps you followed before. You should end up with something like this.

   

2. Next, add two more files colors.txt and shapes.txt to the same working directory. Add a third commit to record the current state of the working directory.

   

6. See the revision graph: Note how commits form a path-like structure aka the revision tree/graph. In the revision graph, each commit is shown as linked to its 'parent' commit (i.e., the commit before it).

Often, there are files inside the Git working folder that you don't want to revision-control e.g., temporary log files. Follow the steps below to learn how to configure Git to ignore such files.

1. Add a file into your repo's working folder that you supposedly don't want to revision-control e.g., a file named temp.txt. Observe how Git has detected the new file.

2. Tell Git to ignore that file:

temp.txt

temp.txt

RCS tools store the history of the working directory as a series of commits. This means you should commit after each change that you want the RCS to 'remember'.

Each commit in a repo is a recorded point in the history of the project that is uniquely identified by an auto-generated hash e.g. a16043703f28e5b3dab95915f5c5e5bf4fdc5fc1.

You can tag a specific commit with a more easily identifiable name e.g. v1.0.2.

To see what changed between two points of the history, you can ask the RCS tool to diff the two commits in concern.

To restore the state of the working directory at a point in the past, you can checkout the commit in concern. i.e., you can traverse the history of the working directory simply by checking out the commits you are interested in.

Each Git commit is uniquely identified by a hash e.g., d670460b4b4aece5915caf5c68d12f560a9fe3e4. As you can imagine, using such an identifier is not very convenient for our day-to-day use. As a solution, Git allows adding a more human-readable tag to a commit e.g., v1.0-beta.

Here's how you can tag a commit in a local repo (e.g. in the samplerepo-things repo):

SourceTree

Right-click on the commit (in the graphical revision graph) you want to tag and choose Tag….

Specify the tag name e.g. v1.0 and click Add Tag.

The added tag will appear in the revision graph view.

---

CLI

To add a tag to the current commit as v1.0,

git tag –a v1.0

To view tags

git tag

To learn how to add a tag to a past commit, go to the ‘Git Basics – Tagging’ page of the git-scm book and refer the ‘Tagging Later’ section.

Remember to push tags to the repo. A normal push does not include tags.

# push a specific tag
git push origin v1.0b

# push all tags
git push origin --tags

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After adding a tag to a commit, you can use the tag to refer to that commit, as an alternative to using the hash.

Git can show you what changed in each commit.

SourceTree

To see which files changed in a commit, click on the commit. To see what changed in a specific file in that commit, click on the file name.

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CLI

git show < part-of-commit-hash >

Example:

git show 251b4cf

commit 5bc0e30635a754908dbdd3d2d833756cc4b52ef3
Author: … < … >
Date:   Sat Jul 8 16:50:27 2017 +0800

    fruits.txt: replace banana with berries

diff --git a/fruits.txt b/fruits.txt
index 15b57f7..17f4528 100644
--- a/fruits.txt
+++ b/fruits.txt
@@ -1,3 +1,3 @@
 apples
-bananas
+berries
 cherries

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Git can also show you the difference between two points in the history of the repo.

SourceTree

Select the two points you want to compare using Ctrl+Click. The differences between the two selected versions will show up in the bottom half of SourceTree, as shown in the screenshot below.

The same method can be used to compare the current state of the working directory (which might have uncommitted changes) to a point in the history.

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CLI

The diff command can be used to view the differences between two points of the history.

• git diff: shows the changes (uncommitted) since the last commit.
• git diff 0023cdd..fcd6199: shows the changes between the points indicated by commit hashes.
  Note that when using a commit hash in a Git command, you can use only the first few characters (e.g., first 7-10 chars) as that's usually enough for Git to locate the commit.
• git diff v1.0..HEAD: shows changes that happened from the commit tagged as v1.0 to the most recent commit.

Resources

• Git-Tower Tutorial: Inspecting Changes with Diffs
• How to view the next page of a git command result

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---

Git can load a specific version of the history to the working directory. Note that if you have uncommitted changes in the working directory, you need to stash them first to prevent them from being overwritten.

SourceTree

Double-click the commit you want to load to the working directory, or right-click on that commit and choose Checkout....

Click OK to the warning about ‘detached HEAD’ (similar to below).

The specified version is now loaded to the working folder, as indicated by the HEAD label. HEAD is a reference to the currently checked out commit.

If you checkout a commit that comes before the commit in which you added the .gitignore file, Git will now show ignored files as ‘unstaged modifications’ because at that stage Git hasn’t been told to ignore those files.

To go back to the latest commit, double-click it.

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CLI

Use the checkout <commit-identifier> command to change the working directory to the state it was in at a specific past commit.

• git checkout v1.0: loads the state as at commit tagged v1.0
• git checkout 0023cdd: loads the state as at commit with the hash 0023cdd
• git checkout HEAD~2: loads the state that is 2 commits behind the most recent commit

For now, you can ignore the warning about ‘detached HEAD’.

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Remote repositories are repos that are hosted on remote computers and allow remote access. They are especially useful for sharing the revision history of a codebase among team members of a multi-person project. They can also serve as a remote backup of your codebase.

It is possible to set up your own remote repo on a server, but the easier option is to use a remote repo hosting service such as GitHub or BitBucket.

When you clone from a repo, the original repo is commonly referred to as the upstream repo. A repo can have multiple upstream repos. For example, let's say a repo repo1 was cloned as repo2 which was then cloned as repo3. In this case, repo1 and repo2 are upstream repos of repo3.

Cloning, pushing, and pulling can be done between two local repos too, although it is more common for them to involve a remote repo.

Given below is an example scenario you can try yourself to learn Git cloning.

Suppose you want to clone the sample repo samplerepo-things to your computer.

Here's a scenario you can try in order to learn how to pull commits from another repo to yours.

1. Clone a repo (e.g., the repo used in [Git & GitHub → Clone]) to be used for this activity.

2. Delete the last few commits to simulate cloning the repo a few commits ago.

SourceTree

Right-click the target commit (i.e. the commit that is 2 commits behind the tip) and choose Reset current branch to this commit.

Choose the Hard - … option and click OK.

This is what you will see.

Note the following (cross refer the screenshot above):

Arrow marked as a: The local repo is now at this commit, marked by the master label.
Arrow marked as b: The origin/master label shows what is the latest commit in the master branch in the remote repo.

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CLI

Use the reset command to delete commits at the tip of the revision history.

git reset --hard HEAD~2

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Now, your local repo state is exactly how it would be if you had cloned the repo 2 commits ago, as if somebody has added two more commits to the remote repo since you cloned it.

3. Pull from the other repo: To get those missing commits to your local repo (i.e. to sync your local repo with upstream repo) you can do a pull.

SourceTree

Click the Pull button in the main menu, choose origin and master in the next dialog, and click OK.

Now you should see something like this where master and origin/master are both pointing the same commit.

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CLI

git pull origin

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You can also do a fetch instead of a pull in which case the new commits will be downloaded to your repo but the working directory will remain at the current commit. To move the current state to the latest commit that was downloaded, you need to do a merge. A pull is a shortcut that does both those steps in one go.

Given below is a scenario you can try in order to learn how to fork a repo:.

0. Create a GitHub account if you don't have one yet.

1. Go to the GitHub repo you want to fork e.g., samplerepo-things

2. Click on the button on the top-right corner. In the next step, choose to fork to your own account or to another GitHub organization that you are an admin of.

GitHub does not allow you to fork the same repo more than once to the same destination. If you want to re-fork, you need to delete the previous fork.

Given below is a scenario you can try in order to learn how to push commits to a remote repo hosted on GitHub:

1. Fork an existing GitHub repo (e.g., samplerepo-things) to your GitHub account.

2. Clone the fork (not the original) to your computer.

3. Commit some changes in your local repo.

4. Push the new commits to your fork on GitHub

SourceTree

Click the Push button on the main menu, ensure the settings are as follows in the next dialog, and click the Push button on the dialog.

Tags are not included in a normal push. Remember to tick Push all tags when pushing to the remote repo if you want them to be pushed to the repo.

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CLI

Use the command git push origin master. Enter your Github username and password when prompted.

Tags are not included in a normal push. To push a tag, use this command: git push origin <tag_name> e.g. git push origin v1.0

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You can push to repos other than the one you cloned from, as long as the target repo and your repo have a shared history.

1. Add the GitHub repo URL as a remote, if you haven't done so already.
2. Push to the target repo.

You can even push an entire local repository to GitHub, to form an entirely new remote repository. For example, you created a local repo and worked with it for a while but now you want to upload it onto GitHub (as a backup or to share it with others). The steps are given below.

1. Create an empty remote repo on GitHub.

1. Login to your GitHub account and choose to create a new Repo.
   

2. In the next screen, provide a name for your repo but keep the Initialize this repo ... tick box unchecked.
   

3. Note the URL of the repo. It will be of the form https://github.com/{your_user_name}/{repo_name}.git.
   e.g., https://github.com/johndoe/foobar.git (note the .git at the end)
   

2. Add the GitHub repo URL as a remote of the local repo. You can give it the name origin (or any other name).

3. Push the repo to the remote.

SourceTree

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CLI

Push each branch to the new remote the usual way but use the -u flag to inform Git that you wish to i.e., remember which branch in the remote repo corresponds to which branch in the local repotrack the branch.
e.g., git push -u origin master

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Professional software engineers often write code using Integrated Development Environments (IDEs). IDEs support most development-related work within the same tool (hence, the term integrated).

An IDE generally consists of:

• A source code editor that includes features such as syntax coloring, auto-completion, easy code navigation, error highlighting, and code-snippet generation.
• A compiler and/or an interpreter (together with other build automation support) that facilitates the compilation/linking/running/deployment of a program.
• A debugger that allows the developer to execute the program one step at a time to observe the run-time behavior in order to locate bugs.
• Other tools that aid various aspects of coding e.g. support for automated testing, drag-and-drop construction of UI components, version management support, simulation of the target runtime platform, and modeling support.

Examples of popular IDEs:

• Java: Eclipse, Intellij IDEA, NetBeans
• C#, C++: Visual Studio
• Swift: XCode
• Python: PyCharm

Some web-based IDEs have appeared in recent times too e.g., Amazon's Cloud9 IDE.

Some experienced developers, in particular those with a UNIX background, prefer lightweight yet powerful text editors with scripting capabilities (e.g. Emacs) over heavier IDEs.

Running IntelliJ IDEA for the First Time

When testing, you execute a set of test cases. A test case specifies how to perform a test. At a minimum, it specifies the input to the software under test (SUT) and the expected behavior.

Test cases can be determined based on the specification, reviewing similar existing systems, or comparing to the past behavior of the SUT.

For each test case you should do the following:

1. Feed the input to the SUT
2. Observe the actual output
3. Compare actual output with the expected output

A test case failure is a mismatch between the expected behavior and the actual behavior. A failure indicates a potential defect (or a bug), unless the error is in the test case itself.

When you modify a system, the modification may result in some unintended and undesirable effects on the system. Such an effect is called a regression.

Regression testing is the re-testing of the software to detect regressions. Note that to detect regressions, you need to retest all related components, even if they had been tested before.

Regression testing is more effective when it is done frequently, after each small change. However, doing so can be prohibitively expensive if testing is done manually. Hence, regression testing is more practical when it is automated.

A simple way to semi-automate testing of a CLI (Command Line Interface) app is by using input/output re-direction.

• First, you feed the app with a sequence of test inputs that is stored in a file while redirecting the output to another file.
• Next, you compare the actual output file with another file containing the expected output.

Let's assume you are testing a CLI app called AddressBook. Here are the detailed steps:

1. Store the test input in the text file input.txt.

   Example input.txt

   ---

2. Store the output you expect from the SUT in another text file expected.txt.

   Example expected.txt

   ---

3. Run the program as given below, which will redirect the text in input.txt as the input to AddressBook and similarly, will redirect the output of AddressBook to a text file output.txt. Note that this does not require any code changes to AddressBook.

   java AddressBook < input.txt > output.txt
   

   • The way to run a CLI program differs based on the language.
     e.g., In Python, assuming the code is in AddressBook.py file, use the command
     python AddressBook.py < input.txt > output.txt

   • If you are using Windows, use a normal command window to run the app, not a PowerShell window.

   More on the > operator and the < operator extra

4. Next, you compare output.txt with the expected.txt. This can be done using a utility such as Windows' FC (i.e. File Compare) command, Unix's diff command, or a GUI tool such as WinMerge.

   FC output.txt expected.txt
   

Note that the above technique is only suitable when testing CLI apps, and only if the exact output can be predetermined. If the output varies from one run to the other (e.g. it contains a time stamp), this technique will not work. In those cases, you need more sophisticated ways of automating tests.