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COMP6209 automated code generation

{`COMP6209 Assignment Instructions
University of Southampton
School of electronics and Computer Science 


The goal of this coursework is to use AspectJ in order to profile Java programs. It consists of two parts. The first part is concerned with qualitative properties and aims at constructing the dynamic call graph of a program. The second part considers quantitative properties, collecting the execution time of certain methods.

Part 1: Dynamic call-graph construction

We define the call graph of a Java program as a directed graph whose nodes are method signatures. There is a node for each method that is called, and an edge from node mi to node mj if method mj is called within the definition of method mi. We say that a call graph is dynamic if it is built at runtime, when nodes and edges are added during program execution.

Write an aspect that constructs the dynamic call graph of a Java program restricted to all public methods that take one argument int, return an int, and are defined in any class within the package called q1 or any of the packages within the q1 hierarchy.

For instance, consider the class:

package q1; 
public class A { 
public int foo(int a) { 
return 0;
private void bar() { 
public int baz(int a) {
return a + a;

The dynamic call graph upon invoking A.foo would have no edges because A.foo does not invoke any other method of interest within its definition. Instead, with the class

package q1; 
public class B { 
public int foo(int a) { 
return 0;
} public int bar(int b) {
return baz(b);
public int baz(int a) {
return a + a;

the dynamic call graph upon invoking B.foo would have two edges

B.foo(int) -> B.bar(int) and B.bar(int) -> B.baz(int).

After the execution of the program, the aspect should produce the following output:

  1. A file in comma-separated value format named q1-nodes.csv with the list of nodes of the dynamic call graph (one node per line).
  2. A file in comma-separated value format named q1-edges.csv where each line consists of a pair source method, target method which represents an edge in the call graph.

Organise your code in a package named q1.<your student id>.

Part 2: Refined call-graph construction

Write an aspect that refines your solution to Part 1 by not providing an arc from mi to mj if mj if mj throws an exception instance of java.lang.Exception when called within the definition of mi.

Organise your code in a package named q2.<your student id>.

Part 3: Runtime profiling

Write an aspect to obtain an input/output summary of the methods identified in Part 1.

  1. For each method, the aspect should compute a histogram of the frequency of the values of the int parameter passed as an input as well as of the returned int At the end of the program execution, this information should be saved to files named <method signature>-hist.csv (i.e., one file for each method) using a comma separated values format.
  2. The aspect should compute the frequency of failures, defined as the percentage of times that the method does not return an int due to throwing an exception.

This information should be outputted to a file named failures.csv in commaseparated values format, where each line provides the failure frequency of a method.

  1. Finally, for each method the aspect should compute the average execution time and its standard deviation across all its invocations.

This information should be outputted to a file named runtimes.csv in commaseparated values format (one line for each method).

Organise your code in a package named q3.<your student id>.


Please submit well-documented AspectJ source code. Do not submit any Java source files. We will test your aspects on our own class files.

Please submit using the C-Bass electronic hand-in system (http://handin.ecs.soton.ac.uk) by 5pm on the due date.

Relevant Learning Outcomes

  1. Aspect-oriented programming techniques

Marking Scheme





Part 1

AspectJ code for call-graph construction


8 marks

Part 2

AspectJ code for refined call-graph construction


4 marks

Part 3

AspectJ code for execution runtime profiling


8 marks

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