A Software Code Complexity Framework; Based on an Empirical Analysis of Software Cognitive Complexity Metrics using an Improved Merged Weighted Complexity Measure

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Volume 4, No. 8, May-June 2013

International Journal of Advanced Research in Computer Science

RESEARCH PAPER

Available Online at www.ijarcs.info

ISSN No. 0976-5697

A Software Code Complexity Framework; Based on an Empirical Analysis of Software

Cognitive Complexity Metrics using an Improved Merged Weighted Complexity Measure

Dr Wafula Joseph Director ICT JKUAT – Juja, Kenya.

Dr. Waweru Mwangi Director - ICSIT JKUAT - Juja, Kenya.

Stephen N. Waweru*

Student - Masters in Software Engineering -

Institute of Computer Science & Information Technology (ICSIT) JKUAT – Juja, Kenya

stiiv2005@yahoo.com

Abstract: This research paper proposes a Software Complexity Code Framework Based on an empirical analysis of Software Cognitive Complexity Metrics using an Improved Merged Weighted Complexity Measure. Software Development Industry in Kenya is dominated by a myriad of Small Software Developers firms. It was observed that majority of the Small Software Developers Organizations have 2 - 20 employees indexed by 62.4%, whereas Large Software Developers Organizations index 30.4% [1]. The increased complexity of modern software applications also increases the difficulty of making the code reliable and maintainable. This research paper measures one internal measure of software products, namely software complexity. I develop a Software Code Complexity Framework using a proposed cognitive complexity metric for evaluating design of object-oriented (OO) code. The proposed metric is based on important features of the Object Oriented Systems: Inheritance, Control Structures, Nesting and Size. The proposed metric is applied on a real project for empirical validation and compared with Chidamber and Kemerer (CK) metrics suite [2]. The practical and empirical validations and the comparative study prove the robustness of the measure. The outcome of this Model leads to a development of Software Code Complexity Framework; a tool-set for static analysis of Java/C/C++ source code: a combination of automatic code review and automatic coding standards enforcement.

Keywords: Software Code complexity, Metric, Framework, Object Oriented System, Cognitive Complexity

I. INTRODUCTION

Object Oriented scheme have come to control software engineering over the last two decades. The improvement and modification in these techniques are still undergoing research [1]. More and more organizations are adopting these techniques into their software development practices. A result of the growth in popularity of object oriented programming is the introduction of number of software design metrics. Although most of these metrics are applicable to all programming languages, some metrics apply to a specific set of programming languages [2], among these metrics some have been proposed based on cognitive complexity called Cognitive Complexity Metrics. Wang [3] observed that the traditional measurements cannot actually reflect the real complexity of software systems in software design, representation, cognition, comprehension, and maintenance. Today, the relevant literature provides a variety of object oriented metrics [4-5], to compute the complexity of software.

The metrics presented in this survey are for Object– oriented programming. Still there are other Object Oriented Cognitive Complexity Metrics specially developed for Object Oriented programs. Cognitive complexity measures are the human effort needed to perform a task difficulty in understanding the software. The cognitive complexity is an ideal measure of software functional complexities and sizes,

because it represents the real semantic complexity by integrating both the operational and architectural complexities. In this research paper, an attempt has been made to develop a very simple Framework for calculating the complexity of code in terms of cognitive weights. This method is the most suitable due to not only its simplness but also it provides the complete information about the information contents of a Program code. There is continuous effort to find a comprehensive complexity metric, which addresses most ofthe parameters of software. The outcome of this research paper leads to automation of the Merged Weighted Complexity Metric including other complexity metrics by developing a; Software Code Complexity Framework;a tool-set for Static Analysis of Object Oriented Systems Source Code. a combination of Automatic Code Review and Automatic Coding Standards Enforcement.

II. SOFTWARE COMPLEXITY

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IV. LI

ere are many res that havee matic number [

o’s data flow re [15],Wang’s omplexity [17] pposed to cov of software a eters. Out of ng a particul m, as every m antages.

COGNITIVE

e field of cogn onal complex

ehension is de re. Basic contr eration [18] is re. The cognit lty or relative software mode fferent architec are in a linear For the form ; are added an BCS’s are mu

ence, 4 (8), May–J

lity is the last c ble 3 and Figu ware code co

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Respondents who c mplexity to Softwar

ITERATURE

y well know e been propo 12], Halstead p w complexity

s cognitive com ]. All the repor ver the correc and to provide f the numero lar complexity measure has i

E WEIGHTS A

nitive informa xity of soft ependent on in rol structures the basic logi tive weight of e time and ef eled by a num ctures for calcu layout or som mer case, sum

nd for the latt ultiplied with

June, 2013,51-62

consideration d ure 4 below ill omplexity with

mpact of Code Com ity

onnect the Impact re Quality

REVIEW

wn software osed such as

programming measures [14 mplexity measu rted complexity ctness, effectiv e good estimat ous proposed

y measure is its own advan

AND INFORM

atics, it is foun tware in de nternal architec (BCS), sequen ic building blo software is th ffort for comp mber of BCS’s.

ulating Wbcs: ei

e BCS’s are em of the weigh ter, cognitive the weights o

53

during their lustrate the h software

mplexity to

of Code

complexity McCabe’s effort [13], 4], Basili’s

ure[16] and y measures veness and

te of these measures, s again a ntages and

MATICS

nd that the esign and cture of the nce, branch ocks of any he extent of prehending . There are ither all the mbedded in hts of all n

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BCS’

’s. The cogniti s under:

Table 5: Basic co

ategory equence ranch

eration

mbedded omponent

oncurrency

THE ANA ISTING COM COGN

According to W d in Cognitive , mechanisms esses, mental esting in comp ain the mechan thinking. It i nitive Informat

lopment of t matics, compu e are a number ognitive Inform plexity Metric ghted Class Co ritance and (v)

his research plexity (WCC

c description, ation in compa plexity Measur

WEIGHTED

Computation

Mishra [20] mod

c called we dered Object on between th med as a set of complexity o plexity of Meth Weighted Cla

Weighted Clas lated using the

re,

ive weights fo

ontrol structures a

BCS Sequence (SEQ If-Then-Else (IT Case

For-do Repeat-until While-do Function Call (F

Recursion (REC Parallel (REC) Interrupt (INT)

ALYSIS AND MPLEXITY M

NITIVE INFO

Wang [16] the e Informatics a of the natur phenomena puting and soft nisms and proc is expected t ics will profou the next gen uting, software

r of metrics wh matics among c (ii) Class C omplexity (iv) Average Comp paper analy C) Metric, it g

its equation, arison to the pr re.

D CLASS COM

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Orientation he Data and F

f data and set o of the class

hods and Attrib ass Complexity E

ss Complexity e equation (1)

r Basic Contro

nd their cognitive

W

FINDINGS O MEASURES B

ORMATICS

major problem are: the archite

ral intelligenc and persona tware engineer cesses of memo that any brea

undly pave the neration tech e, and cogniti hich were deve

them; (i) We Complexity (ii Class comple plexity due inh ses (i) Weig gives an anal research find roposed Merge

MPLEXITY (W

lass Complexity

metric and pro complexity as a form of Function, the c

of method acc should be m

butes. Equation:

y (WCC) [2

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ol Structures

Weight

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ory, learning akthrough in

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20] can be

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Research Findi

his proposed m um of compl

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Weighted Class

eighted Class C internal structu ker’s [19] prop validated and f

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Weighted Cla tandability and ately, it helps th

ation. Howe not only con e number of a er the conc sulation, Overl

THE MERG ME

ftware complex eter of p

imensional at which c m/software are

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statement whic s is harder to

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weight 2 to tho level 3) and so

Types of Co

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ber of Attribute od Complexity

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ings

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Complexity inc ure of methods perties Weighte found that it sa lished this me s Complexity bject Oriented ass Complexi d maintainabili he software de ever a better nsider the inter attributes in a cepts of O loading and Po

GED WEIGHT EASURE (Cw)

xity can not b program/softwa ttribute of so ontribute to e:

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ch is at deepe understand an herwise. We ta ssigning weigh evel/class) i.e in

which are at ose statements w

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ontrol Structur

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ct oriented cod en by the sum

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imitations:

cludes the comp and attributes. ed Class Comp atisfies six pro easure as well, can be used t code with dif ty value ind ty of the code eveloper for be

Object Orien rnal structure class but it s OOP like In

olymorphism.

TED COMPL ) METRIC

be computed b are because oftware. The o complexity

ements in Clas

er level of inh nd thus contrib

ake effect of ht 0 to stateme n the base clas level 2 i.e fi which are in n

res:

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lculated by

de, then the of weights

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Number of

plexity due . By using, plexity has operties out , structured to calculate fferent size. icates that is difficult. etter design nted metric

of method should also Inheritance,

EXITY

by a single e it is

prominent y of a

sses:

heritance of butes more inheritance nts at level ss, weight 1 irst derived

ext derived

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Stephen N. Waweru etal, International Journal of Advanced Research in Computer Science, 4 (8), May–June, 2013,51-62

∑

iterative control structures like for loop, while .. do, do .. while contribute more complexity than decision making control structures like if .. then .. else. Therefore, we assign different weights to different control structures.

C. Nesting of Control Structures:

A statement which is at the deeper most level of nesting ( the inner most level ) of control structures is harder to understand and thus contributes more complexity than otherwise. We also take effect of nesting of control structures by assigning weight 1 to statements at level one i.e. the outer most level, weight 2 for those statements which are at level 2 i.e. the next inner level of nesting and so on. The weight for sequential statements is taken as zero.

D. Size:

Size is also considered one of the parameter of program/class complexity. A class with more methods is harder to understand than a class with less number of methods and hence contributes more complexity [23]. Large programs incur problem just by virtue of volume of information that must be absorbed to understand the program and more resources have to be used in their maintenance. So, size is a factor which adds complexity to a program/class.

By taking these factors into account, a weighted complexity measure for an object-oriented program P was suggested as:

n

C

w

=

E

k

* (W

t

)

k ……. Equation 1

k=1

Where;

Cw - Improved Merged Weighted Complexity Measure [IMWCM] of Program P,

n - Total Number of Executable Statements in Program P,

Σ

- Summation Symbol,

* - Multiplication Symbol,

k - Index Variable,

(Wt)k - Total Weight of kth Executable Statements in Program

P,

ΣEk - Summation of kth Executable Statements in terms of

{Operators, Operands, Methods, Strings & Functions}.

∑

Ek = Eo + Es + Em + Es + Ef ……….………….. (1)

Where;

Eo - Number of Operators in an Executable Statement [P]

Es - Number of Operands in an Executable Statement [P]

Em - Number of Methods in an Executable Statement [P]

Es - Number of Strings in an Executable Statement [P]

Ef - Number of Functions in an Executable Statement [P]

Wt = Wn + Wi + Wc ………. (2)

Where;

Wn = weight due to Nesting Level of Control Structures where it is; = 0 for sequential statements,

= 1 for statements inside the outer most level of control structures,

= 2 for statements inside the next inner level of control structures and so on.

Wi = weight due to Inheritance Level of Statements in classes where it is;

= 0 for statements inside the outer most level of inheritance i.e. inside base class,

= 1 for statements inside the next level of inheritance i.e. first derived class,

= 2 for statements inside the next deeper level of inheritance i.e. next derived class

and so on.

Wc = weight due to Types of Control Structures where it is; = 0 for sequential statements,

= 1 for decision making control statements like if .. then .. else,

= 2 for decision making control statements like while .. do, for loop, do .. while,

= n for switch statement with n cases.

IX. COMPARATIVE STUDY OF THE WEIGHTED COMPLEXITY MEASURE METRIC

In this section, the research paper adapts a ‘C’ program for analysis of the result then calculates the Cognitive Weight Complexity Measure (CWCM) for the program codes. Finally compares Cognitive Weight Complexity Measure with Cognitive Functional Size. The value of Cognitive weight complexity measure and cognitive functional size are given in the following respective tables below..

Table 6: Calculation of Metric Cw for Prog 1- [(S)j]

Statement Number Executable Statement (S)j

s1 void Abc::input() 4

s2 count<<“enter value” 3

s3 cin>>a>>b>>c 7

s4 void Abc::output() 4

s5 count<<“A=“<<a 5

s6 count<<“B=“<<b 5

s7 count<<“C=“<<c 5

Table 7: Calculation of Metric Cw for Prog 1 – [(Wt)j]

Statement Number

Executable Statement

Wa Wi Wc (Wt)j

s1 void Abc::input() 0 1 0 1

s2 count<<“enter value”

0 1 0 1

s3 cin>>a>>b>>c 0 1 0 1

s4 void Abc::output() 0 1 0 1

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s6 void Ab

count<<

he table below ved when I u very low valu des a depend plete complexit herefore the mework based o

de software d led reports f plexity.

Table

Metric Tested

WMC

alculation of Metri

able

ED COMPLEX COMPUTAT

tements) theref

ED COMPLEX ANALYS

w shows that a h used MWCM c ues. This is a dable and reli ty details of a p proposed Sof on MWCM wil developers with

for determinin

e 9: Computation A

Experi

ic Cw for Program

j (Wt)j Cw

XITY MEASU TION

fore; Cw (P) =

XITY MEASU SIS

high complexi compared to o n indication t iable value th program code.

ftware Code ll be very accu

h the actual, ng their soft

Analysis Table

imentation for Pro

CBO MWCM

1 others which that MWCM hat provides

Complexity urate and will realistic and tware codes

gram 1 others feature licensin discus

Und tool fo This t (SciTo which for this

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Figure 5

. THIS RE

hus, we infer Complexity Me ealistic estimat

his is becaus Measure also ta actors not cons McCabe’s meas

hese factors ar heir nesting lev As a software lasses/program o calculate a

lasses/program oftware System

XIII. COM

e comparative d Weighted Co McCabe’s] , CK

erged Weighte ore sensitive velopers to fferences betw alstead’s assu

velopers, wh easure (Cw) ha

ose assumption C was not able f the similar cod

ANALYSIS AN OLS FOR SO

ANALYSIS

ere exist ma rement in the m +. Some of the are proprietary es, support fo ng prices and and analyses U

XV. U

derstand is a r maintaining, tool is devel

ols). This tool extent this too s project.

Language Supp

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5: Computation An

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and compare ms and henc

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PARATIVE I

inspection of omplexity Mea K metrics and H ed Complexity M

measurement, differentiate een codes. umptions ma

ereas Merged as the least am ns are based on e to make sens des had the sam

ND FINDING OFTWARE CO

AND MEASU

any tools fo market, for exa

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UNDERSTAN

commercially measuring an loped by Sci l is thoroughly ol meets with t

port:

nalyze 14 lang nd some web rt. It is availabl olaris.

nalysis Chart

APER FINDIN

posed Merged s complexity

d Weighted C unt the comple r Metrics suite

of control stru

developed in osed measure c e the compl ce of Obje

INSPECTION

f the impleme asure (Cw) ver

Halstead has sho Measure (Cw)

so that it w even small

ay sometimes d Weighted C mount of assum

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GS OF THE EX ODE COMPL UREMENT [4

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ND TOOL

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guages includi programming le for all majo

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Complexity exity due to , LOC and

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ects. ment; most

XISTING LEXITY

46]

alysis and nd Testwell ource while deal in their platforms, arch paper

tic analysis ource code. works Inc. o see up to equirements

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Stephen N. Waweru etal, International Journal of Advanced Research in Computer Science, 4 (8), May–June, 2013,51-62

It can gather large number of metrics including many basic metrics, some advance metrics and some custom metrics. During the evaluation of this tool it is found that some of the commonly used metrics aren't included in this tool, for example is it missing all Halstead metrics. It is also observed that all metrics aren’t available for all the languages the tool can analyzed. However it can gather about 74 metrics and most of the metrics are available for C/C++.

C. Features:

It is a GUI tool for all operating systems this tool supports. It outputs reports in graphical, textual and HTML format. It comes with a programming editor. It has a code check feature that checks the code for coding standards. The coding standards it has for code check are Effective C++ (3rd Edition), MISRA-C 2004 and MISRA-C++ 2008. A very nice feature of this tool is the plug-in support which allows users to define and add new metrics to the functionality.

D. Critical Analysis:

This tool is more useful for the developers than the management. It has features like code check and code editor that aren't generally needed for the management. Although this tool can gather large variety of metrics but it doesn't provide any recommended maximum and minimum values for the metrics. The feature of comparison among various releases of software is also not a part of this tool. Project is created manually and since it has the GUI interface it is maybe hard to automate it. Manually created projects with an in-built feature of code editing allow for unwanted changes to the code and lead to the problems of code security and safety. It has no in-built support for integration with any code repository; therefore code has to be available locally before the analysis can perform. During the analysis on actual code it produced some strange results. Value of 4576376.0000 is calculated for a metric “CountPath” when this tool was analyzing a package "Test". This value is way too strange and with no recommended maximum or minimum guidance, it even becomes more confusing.

XVI. EVALUATION SUMMARY OF ALL TOOLS

The tools evaluation proved that functionality wise the tools differ greatly. Some of the tools try to do too much, from complexity measurements to coding standard checks, from function level to project level, from bug detection to bug prevention and some provide code editing facility as well. On the other side, some tools keep their focus on complexity measurements only with either small or large number of metrics. Language support also varies among tools. Some tools support multiple languages at the same time while others are one language specific tools. Some tools work as standalone but some has integration features with IDE's. Visual Studio has its own complexity measurement feature in it. LOC or its variants and McCabe Cyclomatic complexity are the most common metrics for every tool. The way of creating code analysis reports is also

different in most tools. Most of the tools are designed to help software developers, especially those tools that can be integrated with IDE's and gather large number of metrics.

XVII. PROPOSED SOFTWARE CODE COMPLEXITY FRAMEWORK TOOL BASED ON A MERGED WEIGHTED COMPLEXITY MEASURE METRIC

Among all the tools analyzed in this research paper there were no tools that adapted or used Cognitive Informatics metrics. Therefore this shows that there is a big gap and a great need for a new Software Code Complexity Analysis And Measurement Tool that adapts or is based on Cognitive Weights and Informatics for greater comprehensibility of software codes and entire complete programs at large out there in the market.

Software Code Complexity Framework will provide a way to check your code using;

a. The Improved Metric [Merged Weighted Complexity Measure Cw]

b. Published Coding Standards c. And Custom Standards.

These Checks can be used to verify Naming Guidelines, Metric Requirements, Published Best Practices, or any other Rules or Conventions that are important. Complexity Measurement and Analysis is a process where key figures are derived from existing source code. These key figures then serve as indicators to judge a source code’s quality. Obtaining key figures is accomplished by certain arithmetic’s directly applied to the source code. In General, arithmetics that are applied in complexity measurements are called Metrics. They make up a set of formulae that is provided by code metrics tools. Software Code Complexity Framework [Software Code Complexity Framework] is tool dedicated to measure the complexity of source code written in C, C++ .

A. Published Coding Standards [40]:

You will find many "checks" straight from the following published standards:

i. Effective C++ (3rd Edition) Scott Meyers ii. MISRA-C 2004

iii. MISRA-C++ 2008

B. Software Code Complexity Framework Metrics:

Software Code Complexity Framework is very efficient at collecting metrics about the code it analyzes. These metrics can be extracted automatically via command line calls, viewed graphically, dynamically explored in the GUI, or customized via the Software Code Complexity Framework Java - NetBeans API. They can also be reported at the Project Level, for Files, Classes, Functions or User Defined Architectures. Most of the metrics in Software Code Complexity Framework - can be categorized in the following;

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XXIII. CONCLUSION

The outcome of the Framework developed from the improved merged weighted complexity metric shows that; The tool is capable of performing code analysis automatically on regular basis. It proves that the automatic measurement of source code complexity is possible to implement. The tool is helpful for developers to view the quality of their code in terms of code metrics. Potentially fault-prone code can easily be identified which can suggest developers about the code that requires refactoring. The change of code may also help the testers to focus their testing efforts on those parts of code that are changed. The automatic logging feature will allow for real-time monitoring of the tool. The configuration interface shall allow for simple addition/alteration of configuration specification. As complexity and code size grow for each year, so does the problem of releasing high-quality software. An Improved Weighted Composite Complexity Measure has been used which takes into account different aspects of complexity: size, control structures, their nesting and inheritance level of statements in classes. The research paper shows that the effect of these structures on complexity is quite significant. It is a robust method because it encompasses all major parameters and attributes that are required to find out complexity.

XXIV. REFERENCES

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[9] Curtis, B.: Measurement and Experimentation in Software Engineering. Proc IEEE conference, 68.9. 1144-1157, September 1980.

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[16] Woodward, M. R., Hennel. M. A., David. H.,( 1979) A measure of control flow complexity in program text. IEEE Transaction on Software Engineering, SE-5, Vol. 1, pages 45-50.

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[18] Weyuker. E.J.,(1988), Evaluating software complexity measure. IEEE Transaction on Software Complexity Measure, 14(9): 1357-1365.