ece 453 cs 447 se 465 software testing quality assurance case studies instructor paulo alencar
Download
Skip this Video
Download Presentation
Overview

Loading in 2 Seconds...

play fullscreen
1 / 14

Overview - PowerPoint PPT Presentation


  • 56 Views
  • Uploaded on

ECE 453 – CS 447 – SE 465 Software Testing & Quality Assurance Case Studies Instructor Paulo Alencar. Overview. Introduces a new cohesion metric called Conceptual Cohesion of Classes (C3) and uses this metric for fault prediction

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Overview ' - dwight


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
ece 453 cs 447 se 465 software testing quality assurance case studies instructor paulo alencar

ECE 453 – CS 447 – SE 465 Software Testing & Quality AssuranceCase StudiesInstructorPaulo Alencar

overview
Overview
  • Introduces a new cohesion metric called Conceptual Cohesion of Classes (C3) and uses this metric for fault prediction
  • Compares a new cohesion metric with an extensive set of existing metrics
  • Presents a large cases study on three open source software systems (e.g., Mozilla v.1.6)
  • Marcus, A., Poshyvanyk, D., Ferenc, F., Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems, IEEE Transactions on Software Engineering, vol. 34, no. 2, March/April, 2008.
main results
Main Results
  • Cohesion is usually measured on structural information extracted solely from the source code (e.g., attribute references and method calls)
  • A new measure for class cohesion is proposed (Conceptual Cohesion of Classes – C3) which captures the conceptual aspects of class cohesion
  • C3 is based on the analysis of textual information in the source code, expressed in comments and identifiers
  • An Information Retrieval (IR) technique is used to extract, represent, and analyze the textual information from source code
  • C3 can be interpreted as a measure of the textual coherence of a class within the context of the system
main results1
Main Results
  • Metrics used in comparison:

Lack of Cohesion in Methods

  • LCOM4 – number of connected components (access same class variable); LCOM=1 (good), LCOM >= 2 (bad, split)

(pairs of methods with no attributes in common)

  • LCOM1 – number of pairs of methods that access disjoint sets of instance variables, add 1 to P; or that share at least one instance variable, add 1 to Q:

P –Q if P>0, and 0 otherwise (0 good, >1 may suggest split)

  • LCOM2 – m=#methods, a=# variables, mA=#methods that access the same variable (attribute), sum(mA)= sum over all attributes (0 is good)

LCOM2 = 1 – sum(mA)/m*a

main results2
Main Results
  • LCOM3 = (m – sum(mA) / a ) (m – 1) (1 to 2 good; 0 bad, split)
  • LCOM5 = # of connected components (methods that access the same variable, or call each other)
  • TCC (tight class cohesion) –

NP = maximum number of possible connections = N* (N-1)/2 where N=#methods

NDC # direct connections (#edges in connection graph)

Access the same class variable, or call trees starting at two methods access the same variables

NID # indirect connections (e.g., A-B-C, where A is indirectly connected to C via B)

TCC = NDC / NP (1 good)

main results3
Main Results
  • LCC (loose class cohesion) –

LCC = (NDC + NID) / NP (1 good)

  • Coh – Cohesion metric

Coh = sum(v(A) / m * a

where v(A) = # of methods that reference attribute A

sum(v(A)) = sum over all attributes

(1 good)

  • Information-flow-based cohesion – depends on the number of information flows from/to (i.e., access or read) parameter list to/from data structure due to procedures in interfaces (higher is better)
main results4
Main Results
  • LSI – Latent Semantic Index

- An advanced statistical Information Retrieval method

- Central concept: the information about the concepts in which a particular word appears or does not appear provides a way to determine the similarity of meaning of sets of words to each other

- It uses a matrix (word x context) based on knowledge in the particular domain of interest

main results5
Main Results
  • Conceptual Cohesion of Classes (C3)

- Similarity measure for textual coherence of comments and identifiers extracted from source code

- C3 for a class C measures conceptual cohesion, the degree to which methods of a class belong together conceptually.

- C3 in [0,1] – close to 1 is better

- Close to 1 is better: the class most likely implements a single concept or a very small number of related concepts (related in the context of the software system

- C3 is closet to 0: the methods in the class most likely implement different concepts

main results6
Main Results
  • Example: Class MySecMan (my security manager) from Mozilla v.1.6
  • The four methods share several terms such as context, pending, exception, error, failure, and security
  • C3 = 0.913 (a very high conceptual cohesion for the MySecMan class)
main results7
Main Results
  • Case Studies:

- Three open source systems from different domains (developed mostly in C++)

- TortoiseCVS v.1.8.21 – an extension of Microsoft Windows Explorer that uses CVS

- WinMerge v.2.0.2 – tool for visualization and merging for both files and directories

- Mozilla v.1.6 – an open source Web browser

main results8
Main Results
  • Research questions:

- Question 1: Does C3 capture aspects of class cohesion that are not captured by other structural cohesion metrics?

- Question 2: Does the combination of structural cohesion metrics with C3 provide better results in predicting faults in classes that the combinations of structural metrics?

main results9
Main Results
  • Principal components and metrics (Question 1)

- PC1 is LCOM1, LCOM2, LCOM3, and ICH – metrics that count the number of shared instance variables

- PC4 is C3; PC2 is TCC and LCC; PC3 is LCOM5, PC5 is LCOM3 and LCOM4; PC6 is Coh

main results10
Main Results
  • Predicting faults in classes (Question 2) –

Individual metrics

main results11
Main Results
  • Predicting faults in classes (Question 2) –

Pairs of cohesion metrics

ad