讲解Software Metrics、Java程序语言辅导、讲解Java语言、辅导OOSE Moodle

Object Oriented Software Engineering

Workshop 1

Software Metrics

Objectives

To generate control flow graphs and understand how they are used to determine the

complexity of program code.

Understand how software metrics can be used to measure and improve the quality

of software.

You will achieve these objectives by completing a set of tasks.

Setup 1

Download vcs.zip from OOSE Moodle page and unzip it to a folder of your choice.

Create a Java eclipse project and name it VehicleControlSystem. Copy oose.vcs,

vehicle.types and img folder from unzipped content into the src and project directories

in Eclipse respectively. Your eclipse directory structure should be similar to Figure

1. To run the project, right click Controller.java and run as Java application. A class

Figure 1: Project directory structure for Vehicle Control System on Eclipse

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Figure 2: Design Diagram for a Vehicle Control System

diagram of the system design is as shown in Figure 2. You can also access this diagram by

clicking Design.ucls in oose.vcs package. But you would have installed ObjectAid UML

Explorer plugin by following instructions from http://www.objectaid.com/class-diagram.

Setup 2

Download CKMetrics.jar from OOSE Moodle page to a folder of your choice. CKMetrics

is a tool for determining Chidamber and Kemerer (CK) metrics suite for java class files and

Jar APIs. CKMetrics operates by using Apache Byte Code Engineering Library (BCEL)

to analyse class files that are contained in a jar file. Assume you have A.jar located in

your local drive ../JarLocation. To determine the quality of A using CKMetrics:

1. Select Load File button from the toolbar. You can either browse and select the

folder containing A or select A. For the former, CKMetrics will load all the jar files

contained in the folder.

2. Then select Compute button from the toolbar. CKMetrics will compute the mean

CK metrics value for each selected jar file.

3. Optionally, you can include jdk class files in the analysis, or only analyse publicly

accessible classes, fields and methods by checking includeJdk and onlyPublic

checkboxes respectively.

Figure 3 is a screenshot showing CKMetrics’s output. You can also select a .class file

following above steps to analyse java classes.

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Figure 3: Analysis output from CKMetrics

Finally, to build the jar file required to check the quality of Vehicle Control System

based on above steps:

1. Right click on the project in Eclipse.

2. Select export.

3. Export as runnable jar file.

Tasks

1. Draw the control flow graph for the method initialiseVehicle in Controller.java.

(1 Mark)

2. Assume the objective is to increase encapsulation, modularity and reduce the complexity

of VehicleControlSystem. Identify 3 metrics that can be used to measure

these quality factors in VehicleControlSystem. (1 Mark)

3. Identify the most important class in VehicleControlSystem that should be refactored

to enhance the quality factors in Task 2. Justify your choice in a maximum

of 200 words. (1 Mark)

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4. Propose and implement four refactorings for the class identified in Task 3 to enhance

quality factors mentioned in Task 2. In maximum of 200 words, discuss the extent

suggested refactorings enhances quality factors mentioned in Task 2. (1 Mark)

5. Draw a new class diagram for VehicleControlSystem after implementing suggested

refactorings in Task 4. In maximum of 200 words, discuss any structural difference

in the design of VehicleControlSystem after implementation. (1 Mark)

Deliverables

1. Turn in a pdf document electronically via Moodle for Workshop 1. You should

clearly state at the top of the document your name and registration number. The

document should also contain your solution to tasks 1- 5 with relevant screenshots

of CKMetrics and class diagrams.

2. A source folder for Vehicle Control System containing the implementation of your

refactoring tasks.

3. A runnable jar file of the Vehicle Control System after implementing your proposed

refactoring tasks.

Assessment

Submissions is due by 16.30 on 5th February 2019. You should submit your solution

on Moodle in the appropriate upload slot for the laboratory.

Tutors and demonstrators will be in your allocated Lab to offer assistance. Endeavour

to attend your labs to discuss issues rather than sending emails. Its likely that

you will receive no response by email or response may be slow.

As per the Code of Assessment policy regarding late submissions, submissions will be

accepted for up to 5 working days beyond due date. Any late submissions will be marked

as if submitted on time, but reduced by 1 mark for each additional day. Submissions

received more than 5 working days after the due date will receive an H (band value of 0).

Dr Inah Omoronyia, S152, School of Computing Science, University of Glasgow