Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Ptidej Team, profile picture
Uploaded byPtidej Team
342 views

WCRE09a.ppt

This document presents the results of a study investigating the relationship between code smells and change-proneness in software classes. The study analyzed two large open source projects and found that classes containing code smells were significantly more likely to undergo changes between versions than classes without smells. Certain smells like MessageChains and NotComplex were also found to be strongly correlated with change-proneness. The results provide empirical evidence that code smells negatively impact maintenance by making affected classes more change-prone over time.

Business◦Technology◦
Download to read offline
An Exploratory Study of the Impact of Code Smells on Software Change- proneness Foutse Khomh, Massimiliano Di Penta, and Yann-Gaël Guéhéneuc WCRE’09 2009/10/13 Ptidej Team – OO Programs Quality Evaluation and Enhancement using Patterns Department of Computer Science and Operations Research University of Montréal, Québec, Canada © Khomh, 2009
Context and Motivation Code smells are “poor” solutions to recurring implementation problems Code smells are in between design and implementation Code smells are conjectured in the literature to hinder object-oriented software evolution No previous study has investigated the relationship between code smells and change-proneness 2/27
Objective Study the impact of code smells on the change-proneness of classes Study the impact of the composition of code smells on the change-proneness of classes 3/27
Outline Study Definition Study Context Study Design Analysis Method Results Conclusion and Future Work 4/27
Study Definition (1/2) Goal: investigate the relation between the presence of smells in classes and their change-proneness Quality focus: the increase of maintenance effort and cost is due to the presence of code smells 5/27
Study Definition (2/2) Perspective: both researchers, practitioners, and managers should be aware of the impact of code smells on classes to make inform design and implementation choices – The presence of change-prone classes increases maintenance effort and cost 6/27
Study Context (1/2) Programs – 9 versions of Azureus (5,858,041 LOCs) – 13 versions of Eclipse (31,579,975 LOCs) Change history between each analysed releases in the programs’ concurrent Versions System (CVS) 7/27
Study Context (2/2) 29 code smells Detected using our tool, DECOR (based on 8/27 metrics and lexical information)
Study Design (1/3) RQ1: What is the relation between smells and change proneness? – H01: the proportion of classes undergoing at least one change between two releases does not significantly differ between classes with code smells and other classes. RQ2: What is the relation between the number of smells in a class and its change-proneness? – H02: the numbers of smells in change-prone classes are not significantly higher than the numbers of smells in classes that do not change. 9/27
Study Design (2/3) RQ3: What is the relation between particular kinds of smells and change proneness? – H03: classes with particular kinds of code smells are not significantly more change- prone than other classes. 10/27
Study Design (3/3) Independent Variables – 29 kind of code smells. – Each variable si,j,k indicates the number of times a class i has a smell j in a release rk. Dependent variables – Classes change proneness. – Measured as the number of changes ci,k that a “smelly class” underwent between release rk and subsequent release rk+1. 11/27
Analysis Method (1/3) For RQ1, we: – Test the proportions of classes exhibiting (or not) at least one change with (some) smells vs. other classes. – Use Fisher’s exact test. – We compute the Odds ratios (OR). 12/27
Analysis Method (2/3) For RQ2, we: – Compare the numbers of smells in change- prone classes vs. non-change-prone classes. – Use a Mann-Whitney non-parametric test. – Estimate the magnitude of differences with the Cohen d effect size • Small: 0.2 ≤ d ≤ 0.3, medium: around 0.5, and large: ≥ 0.8. 13/27
Analysis Method (3/3) For RQ3, we: – Relate change-proneness with the presence of particular kinds of smells. – We use a logistic regression model. – We decide that a relation is significant if the null-hypothesis is rejected for at least 75% of the releases. 14/27
Results (1/6) RQ1: smells and changes (Azureus) 15/27
Results (2/6) RQ1: smells and changes (Eclipse) 16/27
Results (3/6) RQ2: composition of smells and changes (Azureus) 17/27
Results (4/6) RQ2: composition of smells and changes (Eclipse) 18/27
Results (5/6) RQ3: kinds of smells and changes (Azureus) 19/27
Results (6/6) RQ3: kinds of smells and changes (Eclipse) 20/27
Discussion Classes with smells are more change- prone, some odds ratio 3 to 8 times bigger for these classes. HasChildren, MessageChains, NotComplex, and NotAbstract lead almost consistently to change-prone classes. Existing smells are generally removed from the system while some new are introduced in the context of new 21/27 features addition.
Threats to the validity (1/2) Construct validity – The count of changes occurred to classes is based on the CVS change log. – Subjectivity of the definitions of code smells • We were interested in code smells as defined by our tool. – Dependence between code smells • We perform a Spearman rank correlation analysis. • Our logistic regression only selected non- 22/27 correlated smells.
Threats to the validity (2/2) Internal validity – We do not claim causation, only relation. Conclusion validity – Statistic tests properly used. • We verified their underlying assumptions. Reliability validity – We provide all the details for the replication of our study. External validity – The generalisation will require further studies. • We chose programs from different domains and a representative set of code smells. 23/27
Conclusion (1/2) We provide empirical evidence of the negative impact of code smells on classes change-proneness. We show that: – Classes with smells are significantly more likely to be the subject of changes, than other classes. – The more a class have smells, the more it changes. – Some specific code smells, are more likely to be of concern during evolution: e.g., MessageChains, NotComplex (and Lazy classes) 24/27
Conclusion (2/2) Previous studies correlated source code metrics with change-proneness. – But, we believe that smells can provide to developers recommendations easier to understand than what metric profiles can do. – However, we found that metrics still perform better than smells on building change- proneness/fault-proneness prediction models. 25/27
Future work Our future work includes: – Replicating this study – Studying the effect of anti patterns which are at a higher level of abstraction – Relates smells, anti patterns to fault- proneness The data of our study are available online http://www.ptidej.net/downloads/experiments/prop-WCRE09 26/27
Questions Thank you for listening 27/27