1 Source Code Documentation

1.1 Comments

• widely used

• widely abused

1.1.1 Do Comments Matter?

1.1.2 Which is better?

double m; // mean average
double s; // standard deviation

double meanAverage
double standardDeviation

1.1.3 Which is better?

// Sum up the data
double sum = 0.0;
double sumSquares = 0.0;
// Add up the sums
for (double d: scores)
{
   sum += d;
   sumSquares += d*d;
}

// Compute the average and standard
//  deviation
double meanAverage = sum / numScores;
double standardDeviation =
   sqrt ((sumSquares - numScores*sum*sum)
            /(numScores - 1.0));

// Subtract the average from each data
// item and divide by the standard
// deviation.
for (int i = 0; i < numScores; ++i)
{
   scores[i] = (scores[i] - meanAverage)
       / standardDeviation;
}

// Compute summary statistics
double sum = 0.0;
double sumSquares = 0.0;

for (double d: scores)
{
   sum += d;
   sumSquares += d*d;
}

double meanAverage = sum / numScores;
double standardDeviation =
   sqrt ((sumSquares - numScores*sum*sum)
            / (numScores - 1.0));

// Normalize the scores
for (int i = 0; i < numScores; ++i)
{
   scores[i] = (scores[i] - meanAverage)
       / standardDeviation;
}

1.1.4 Which is better?

// Compute summary statistics
double sum = 0.0;
double sumSquares = 0.0;

for (double d: scores)
{
   sum += d;
   sumSquares += d*d;
}

double meanAverage = sum / numScores;
double standardDeviation =
   sqrt ((sumSquares - numScores*sum*sum)
            /(numScores - 1.0));

// Normalize the scores
for (int i = 0; i < numScores; ++i)
   scores[i] = (scores[i] - meanAverage)
       / standardDeviation;

void computeSummaryStatistics (
   const double* scores,      // inputs
   int numScores,
   double& meanAverage,       // outputs
   double& standardDeviation)
{
  double sum = 0.0;
  double sumSquares = 0.0;
  for (double d: scores)
  {
	 sum += d;
	 sumSquares += d*d;
  }

  meanAverage = sum / numScores;
  standardDeviation =
	 sqrt ((sumSquares - numScores*sum*sum)
			  /(numScores - 1.0));
}


void normalizeData (double* data,
                    int numData,
                    double center,
					double spread)
{
  for (int i = 0; i < numData; ++i)
    data[i] = (data[i] - center) / spread;
}

    ⋮

double meanAverage;
double standardDeviation;
computeSummaryStatistics (scores, numScores,
    meanAverage, standardDeviation);
normalizeData (scores, numScores,
    meanAverage, standardDeviation);

1.1.5 Which is better?

void computeSummaryStatistics (
   const double* scores,      // inputs
   int numScores,
   double& meanAverage,       // outputs
   double& standardDeviation)
{
  double sum = 0.0;
  double sumSquares = 0.0;

  for (double d: scores)
  {
	 sum += d;
	 sumSquares += d*d;
  }

  meanAverage = sum / numScores;
  standardDeviation =
	 sqrt ((sumSquares - numScores*sum*sum)
			  /(numScores - 1.0));
}


void normalizeData (double* data,
                    int numData,
                    double center,
					double spread)
{
  for (int i = 0; i < numData; ++i)
	 data[i] = (data[i] - center) / spread;
}
    ⋮

double meanAverage;
double standardDeviation;
computeSummaryStatistics (scores, numScores,
    meanAverage, standardDeviation);
normalizeData (scores, numScores,
    meanAverage, standardDeviation);

void computeSummaryStatistics (
   const double* scores,      // inputs
   int numScores,
   double& meanAverage,       // outputs
   double& standardDeviation)
{
  double sum = accumulate(
     scores, scores+numScores);
  double sumSquares = accumulate(
     scores, scores+numScores,
     [](double x, double y)
	   {return x + y*y;});

  meanAverage = sum / numScores;
  standardDeviation =
	 sqrt ((sumSquares - numScores*sum*sum)
			  /(numScores - 1.0));
}


    ⋮

// Normalize the scores
double meanAverage;
double standardDeviation;
computeSummaryStatistics (scores, numScores,
    meanAverage, standardDeviation);
transform (
    scores, scores+numScores,
	scores,
    [] (double d) {
	  return (d - meanAverage)
	           / standardDeviation});

1.1.6 Kinds of Comments

• Repeat of the code

  • Useless

• Explanation of the code

  • Only useful if the code is confusing

  • In which case, first priority should be to simplify the code.

• Markers

  • notes not intended to be left in final code

  • If standardized, useful

    • e.g., // TODO in Eclipse

      • flagged in editor
      • easily searched for

• Summary of the code

  • applied to entire code “paragraphs”
  • useful to allow easy skimming of the code

• Description of intent

  • similar to summary, but describes problem rather than solution

• Information that cannot be expressed in the code

  • e.g., authors, copyright, date of modification

1.2 Self-Documenting Code

Self-Documenting code relies on good programming style to perform most of the documentation.

• “the Holy Grail of legibility” (McConnell)

1.2.1 Characteristics of Self-Documenting Code

Classes

• Does the class’s interface present a consistent abstraction?

• Is the class well named, and does its name describe its central purpose?

• Does the class’s interface make obvious how you should use the class?

• Is the class’s interface abstract enough that you don’t have to think about how its services are implemented? Can you treat the class as a black box?

Routines

• Does each routine’s name describe exactly what the routine does?

• Does each routine perform one well-defined task?

• Have all parts of each routine that would benefit from being put into their own routines been put into their own routines?

• Is each routine’s interface obvious and clear?

Data Names

• Are type names descriptive enough to help document data declarations?

• Are variables named well?

• Are variables used only for the purpose for which they’re named?

• Are loop counters given more informative names than i, j, and k?

• Are well-named enumerated types used instead of makeshift flags or boolean variables?

• Are named constants used instead of magic numbers or magic strings?

• Do naming conventions distinguish among type names, enumerated types, named constants, local variables, class variables, and global variables?

Data Organization

• Are extra variables used for clarity when needed?

• Are references to variables close together?

• Are data types simple so that they minimize complexity?

• Is complicated data accessed through abstract access routines (abstract data types)?

Control

• Is the nominal path through the code clear?

• Are related statements grouped together?

• Have relatively independent groups of statements been packaged into their own routines?

• Does the normal case follow the if rather than the else?

• Are control structures simple so that they minimize complexity?

• Does each loop perform one and only one function, as a well-defined routine would?

• Is nesting minimized?

• Have boolean expressions been simplified by using additional boolean variables, boolean functions, and decision tables?

Layout

• Does the program’s layout show its logical structure?

Design

• Is the code straightforward, and does it avoid cleverness?

• Are implementation details hidden as much as possible?

• Is the program written in terms of the problem domain as much as possible rather than in terms of computer-science or programming-language structures?

(McConnell, ch 32)

1.3 Charting

How many forms of software documentation charting do you know?

• Control Flow

  • Flowcharts

  • Nassi-Schneidermann Charts

  • State diagrams
  • UML interaction diagrams
• Module relationships

  • Structure (call) charts

  • Data-Flow Diagrams

  • SADT (Structured Analysis and Design Technique)
  • E-R
  • UML class relationship diagrams

1.3.1 From Code to Charts

1.3.2 From Charts to Code

A hallmark of so-called CASE (Computer-Aided Software Engineering) systems

• Modern versions generate class declarations from UML class diagrams

2 API Documentation

API documentation tools are now more common

• Reflect modern emphasis on re-usable interfaces

• Combine info from

  • a (limited) language parser

    • Extracts info about module/function structure and function parameters

  • and specially formatted blocks of comments embedded in the source code

• Encourages updating comments as code is modified

  Comments become a legitimately useful tool for application writers.

  • Application writers have less need to access actual code.

  • Generate linked documents to facilitate browsing of referenced type names and other entities

  • Some IDEs understand this markup as well and use it enhance “live” help while editing code.

2.1 javadoc

Perhaps the best known tool in this category

2.1.1 Javadoc Comments

2.3 Other API Documentation Generators

Because a documentation generator needs to module and function structure and function parameters, a distinct parser is needed for each programming language.

This leads to a variety of language-specific tools, e.g.,

• jsDoc for Javascript

• YARD for Ruby

• sandcastle for .Net

3 Project Reports

3.1 Test Reports

3.2 Static Code Analyzers

Many tools that we will cover later for analyzing code can produce useful (or at least, impressive) documentation as a side effect.

3.3 Configuration Reports

Configuration managers (to be covered later) generate reports about the dependencies among the software components.

Examples:

4 Project Websites

4.1 Publishing to a website

You can also add instructions to your build manager to post files to a website.

Common cases:

1. The web server is on the same file system as the development machine.

   e.g., You are developing on atria.cs.odu.edu and can publish web pages on http://www.cs.odu.edu/~yourName by simply placing files in ~yourName/public_html.

2. The web server is on the a separate file system, possibly a spearate local network, from the development machine.

   • You will need an account on the web server through which you can do ftp or sftp file transfers.

3. Continuous Integration servers often integrate web services for automatically generated reports.

We’ll look at examples of the first two cases now, and the third later n the semester.

4.1.1 Shared File System

4.1.2 Copy over network