Landman, D.

Serebrenik, A.

Vinju, J.J.

English

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 The relationship between CC and SLOC : a preliminaryanalysis on its evolutionCitation for published version (APA):Landman, D., Serebrenik, A., & Vinju, J. J. (2014). The relationship between CC and SLOC : a preliminaryanalysis on its evolution. In M. Bruntink, & T. Storm (Eds.), Benevol 2014 (Seminar on Software Evolution inBelgium and the Netherlands, Amsterdam, The Netherlands, November 27-28, 2014) (pp. 29-30). Centrum voorWiskunde en Informatica.Document status and date:Published: 01/01/2014Document Version:Accepted manuscript including changes made at the peer-review stagePlease check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. 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Nov. 2023The relationship between CC and SLOC:a preliminary analysis on its evolutionDavy Landman∗, Alexander Serebrenik∗†, Jurgen Vinju∗†‡∗ Centrum Wiskunde & Informatica, Amsterdam, The Netherlands{Davy.Landman, Jurgen.Vinju}@cwi.nl† Eindhoven University of Technology, Eindhoven, The Netherlandsa.serebrenik@tue.nl‡ INRIA Lille Nord Europe, Lille, FranceI. INTRODUCTIONIs it useful to measure both Cyclomatic Complexity (CC)and Source Lines of Code (SLOC)? In previous work [1] wehave analyzed the reported linear relationship between CC andSLOC. In our large corpus of Java projects, we could not findsuch a linear relationship. Raising questions for future work.Object Oriented Programming (OOP) could cause the lack ofa linear relationship between CC and SLOC. In an OO language,dynamic dispatch and polymorphism are used as an alternativeto control flow statements. However, related work [2], [3]reported linear relationships for both C++ and Java.As identified in our earlier work, there is an open questionof the evolution of this relationship. Therefore we explorer apossible evolutionary argument: are the Java programs of todayusing more OOP? And does this cause the decreased power ofSLOC to predict CC?II. RESEARCH METHODAs a preliminary study of the evolution of this relationship,we select one software systems and calculate CC and SLOC foreach method over the last 10 years. To summarize over thisperiod, we sample only the methods in the system at the endof every full year (e.g. 2003–2013 range).A. HypothesisRelated work measured the linear relationship with Pearson’scorrelation (R2). Similarly to our previous work [1], we willcalculate both the Pearson and Spearman correlation. ThePearson correlation will be calculated before and after a powertransform. Moreover, we will also perform the Breusch-Pagantest [4] to confirm non constant variance (heteroscedasticity).We have formulated the following two hypothesis.Hypothesis 1. Older revisions of a software system have astronger linear (Pearson) correlation between the CC and SLOCmetrics for Java methods then newer revisions.Hypothesis 2. Older revisions of a software system do haveconstant variance between the CC and SLOC metrics for Javamethods.B. SystemWe have selected a single system out of the Qualitas Corpus,DrJava. It was selected due to its domain and age. It is a JavaIntegrated Development Environment (IDE) with over 3000revisions since 2000. The system grew from 30 K SLOC in2003 to 200 K SLOC in 2013. We chose an IDE since theycontain elements of multiple domains.C. Measuring SLOC and CCWe use the same tools as in our previous study [1]. EclipseJDT is used to parse Java methods into Abstract Syntax Tree(AST) form. This AST is visited and for each node that wouldgenerate a fork in the Java control flow graph, 1 is added tothe CC of that method. For SLOC we use RASCAL to tokenizeJave into newlines, whitespace, comments and other words.These tokens are then used to calculate the SLOC of a method.III. RESULTSA. CorrelationTable I contains the Pearson correlations before and afterpower transform, Spearman’s correlation, and if the linearmodel was heteroskedastic.TABLE ICORRELATIONS BETWEEN CC AND SLOC FOR A PERIOD OF 10 YEARS ANDTHE TOTAL NUMBER OF METHODS IN THAT REVISION. ALL CORRELATIONSHAVE A HIGH SIGNIFICANCE LEVEL (p ≤ 1 × 10−16). HETROSKEDASTICY ISCHECKED BY THE BREUSCH-PAGAN TEST (IN ALL CASES p ≤ 1 × 10−16).Year Methods R2 log R2 ρ Heteroscedastic2003 3090 0.45 0.45 0.65 Yes2004 4812 0.45 0.47 0.66 Yes2005 9859 0.59 0.52 0.70 Yes2006 10 262 0.56 0.47 0.67 Yes2007 13 784 0.34 0.38 0.62 Yes2008 14 998 0.35 0.39 0.62 Yes2009 17 466 0.43 0.39 0.61 Yes2010 19 765 0.44 0.40 0.61 Yes2011 20 421 0.43 0.41 0.62 Yes2012 20 470 0.42 0.42 0.63 Yes2013 20 476 0.42 0.42 0.63 YesB. Scatter plotsFigure 2 shows a zoomed-in (CC ≤ 20 and SLOC ≤ 50)scatter-plot of the methods of DrJava in 2003 and 2013. Due to(a) 2003 (b) 2013Fig. 1. Residual plot of the linear regression after the power transform, both axis are on a log scale. The non-constant variance complicates the interpretationof the linear regressions.(a) 2003 (b) 2013Fig. 2. Scatter plots of SLOC vs CC. The solid and dashed lines are the linear regression before and after the power transform.the skewed-data, this figure still shows 98% of all data points.The two gray lines in the figure shows the linear regressionsbefore and after the power transform. The gray scale gradientof the points in the scatter-plot visualizes how many methodshave that combination of CC and SLOC: the darker, the moredata points.IV. ANALYSISA. Hypothesis 1: correlation in older revisionsIn Table I we see that although R2 fluctuates over the yearsof DrJava’s development, it remains near the 0.40, with theexception of 2005 and 2006. The increase in correlation couldperhaps be explained by the big growth in 2005. However, wecannot confirm Hyptothesis 1, older versions of the softwaredo not have a higher correlation.B. Hypothesis 2: constant variance in older revisionsTable I shows that for all years the relation between CCand SLOC has non constant variance. The scatter plots inFigure 2 also visualize this growing variance, further shown inthe residual plots in Figure 1. Therefore, we cannot confirmHypothesis 2.V. DISCUSSIONWe have presented a preliminary study on the evolution ofthe relationship between CC and SLOC. In the software systemwe analyzed, we did not observe a obvious change the linearityof the relationship. We also found that the heteroscedasticityreported in our previous work was present all versions ofDrJava. Hetroscedasticity further complicates the inpretationof linear models.In this abstract we have presented the evolution of one system.For future work, we would like to analyse the evolution of awhole corpus over the span of at least 10 years. Moreover, weare interested what other variables we might be measuring bycomparing older version of the system with newer versions.REFERENCES[1] D. Landman, A. Serebrenik, and J. Vinju, “Empirical analysis of therelationship between CC and SLOC in a large corpus of Java methods,”in 30th IEEE International Conference on Software Maintenance andEvolution, ICSME 2014, 2014.[2] K. E. Emam, S. Benlarbi, N. Goel, and S. N. Rai, “The confounding effectof class size on the validity of object-oriented metrics,” IEEE Transactionson Software Engineering, vol. 27, no. 7, pp. 630–650, 2001.[3] G. Jay, J. E. Hale, R. K. Smith, D. P. Hale, N. A. Kraft, and C. Ward,“Cyclomatic Complexity and Lines of Code: Empirical Evidence of a StableLinear Relationship,” Journal of Software Engineering and Applications,vol. 2, no. 3, pp. 137–143, 2009.[4] T. Breusch and A. Pagan, “A simple test for heteroscedasticity and randomcoefficient variation,” Econometrica, vol. 47, no. 5, pp. 1287–1294, Sep.1979.

The relationship between CC and SLOC : a preliminary analysis on its evolution

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The relationship between CC and SLOC : a preliminary analysis on its evolution

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