1 / 14

Overview

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

dwight
Download Presentation

Overview

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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.

  3. 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

  4. 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

  5. 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)

  6. 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)

  7. 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

  8. 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

  9. 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)

  10. 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

  11. 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?

  12. 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

  13. Main Results • Predicting faults in classes (Question 2) – Individual metrics

  14. Main Results • Predicting faults in classes (Question 2) – Pairs of cohesion metrics

More Related