Software Metrics. Lines of Code

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1 Software Metrics Naveed Arshad Lines of Code The total number of lines of executable code in the software program or module being measured But lines of code could mean anything e.g. count only executable lines count executable lines plus data definitions. count executable lines, data definitions, and comments. count lines as physical lines on an input screen. count lines as terminated by logical delimiters. 1

2 Advantages and drawbacks of LOC Advantages of LOC Simple to measure Drawbacks of LOC It is defined on code. For example it cannot measure the size of specification. It characterise only one specific view of size, namely length, it takes no account of functionality or complexity Bad software design may cause excessive line of code It is language dependent Users cannot easily understand it McCabe s Cyclomatic Number Used to find complexity of a given program. V(G) = e n + p 2

3 Advantages of McCabe Cyclomatic Complexity It can be used as a ease of maintenance metric. Used as a quality metric, gives relative complexity of various designs. It can be computed early in life cycle than of Halstead's metrics. Measures the minimum effort and best areas of concentration for testing. It guides the testing process by limiting the program logic during development. Is easy to apply. Drawbacks of McCabe Cyclomatic Complexity The cyclomatic complexity is a measure of the program's control complexity and not the data complexity The same weight is placed on nested and non-nested loops. However, deeply nested conditional structures are harder to understand than nonnested structures. It may give a misleading figure with regard to a lot of simple comparisons and decision structures. Whereas the fan-in fan-out method would probably be more applicable as it can track the data flow 3

4 Halstead Complexity Measures n1 = the number of distinct operators n2 = the number of distinct operands N1 = the total number of operators N2 = the total number of operands Halstead Volume N is program length N = total number of operators N1 + total number of operands N2 n is program vocabulary N = number of distinct operators n1 + number of distinct operands n2 4

5 Halstead Complexity Numbers Measure Program length Program vocabulary Volume Difficulty Effort Symbol N n V D E Formula N= N1 + N2 n= n1 + n2 V= N * Log 2 (n) D= (n1/2) * (N2/n2) E= D * V Halstead Complexity Numbers A FORTRAN subroutine that sorts an array into ascending order SUBROUTINE SORT (X, N) INTEGER X(100), N, I, J, SAVE, IM1 C THIS ROUTINE SORTS ARRAY X INTO ASCENDING C ORDER IF(N.LT. 2) GO TO 220 DO 210 I = 2,N IM1 = I - 1 DO 200 J = 1,IM1 IF(X(I).GE. X(J)) GO TO 200 SAVE = X(I) X(I) = X(J) X(J) = SAVE 200 CONTINUE 210 CONTINUE 220 RETURN END 5

6 Halstead Complexity Numbers A FORTRAN subroutine that sorts an array into ascending order SUBROUTINE SORT (X, N) INTEGER X(100), N, I, J, SAVE, IM1 C THIS ROUTINE SORTS ARRAY X INTO ASCENDING C ORDER IF(N.LT. 2) GO TO 220 DO 210 I = 2,N IM1 = I - 1 DO 200 J = 1,IM1 IF(X(I).GE. X(J)) GO TO 200 SAVE = X(I) X(I) = X(J) X(J) = SAVE 200 CONTINUE 210 CONTINUE 220 RETURN END Operators Occurrences Operands Occurrences SUBROUTINE 1 SORT 1 () 10 X 8, 8 N 4 INTEGER IF 2 I 6.LT. 1 J 5 GOTO 2 SAVE 3 DO 2 IM1 3 = GE CONTINUE RETURN End-of-line 13 n1 = 14 N1 = 51 n2 = 13 N2 = 42 Halstead s Software Complexity - 6 Program Length, N = N1 + N2 = 93 Program Vocabulary, n = n1 + n2 = 27 Program Volume, V = N * log 2 n = 93 * log 2 27 = Represents storage required for a binary translation of the original program Estimates the number of mental comparisons required n1 = 14 N1 = 51 n2 = 13 N2 = 42 6

7 Advantages of Halstead Do not require in-depth analysis of programming structure. Predicts rate of error. Predicts maintenance effort. Useful in scheduling and reporting projects. Measure overall quality of programs. Simple to calculate. Can be used for any programming language. Numerous industry studies support the use of Halstead in predicting programming effort and mean number of programming bugs. Drawbacks of Halstead It depends on completed code. It has little or no use as a predictive estimating model. But McCabe's model is more suited to application at the design level. 7

8 Defects per Thousand Lines of Code Effective bug tracking can help build a record of the known bugs within a system. When applied against measures of software scope (e.g., lines of code or function points), they can give an indication of the.buggy-ness. of the software. SEI Maintainability Index Maintainability M = * log2(avev) * avev(g ) * log2 (aveloc) + 50 * sin (sqrt(2.4 * percm)) The coefficients are derived from actual usage. The terms are defined as follows: avev = average Halstead Volume V per module avev(g) = average cyclomatic complexity per module aveloc = the average count of lines of code (LOC) per module; and, optionally percm = average percent of lines of comments per module 8

9 Lack of Cohesion of Methods LCOM is a measure of how close methods are to the data they access. The more attributes of a class each method accesses (or, more specifically, the more methods that access each attribute), the lower the value of LCOM and the more cohesive the class is said to be. It.s calculated by taking the average number of methods accessing the attributes of a class, minus the total number of methods of that class, divided by 1 minus the number of methods. LCOM = ((1/a * 2 A). m)/(1. m) Where a is the number of attributes of the class, 2 A is the sum across the set of attributes of the number of methods that access each attribute, and m is the number of methods of the class. Instability Afferent couplings (Ca). the number of modules in other components that depend on modules in this component Efferent couplings (Ce). the number of modules in other components this modules in this component depends on Instability, I = Ce / (Ca + Ce) When I = 0, the component is said to be maximally stable 9

10 Some Other Software Measures Length of source code (measured by LOC) Duration of testing process (measured by elapsed time in hours) Number of defects discovered during the testing process (measured by counting defects) Effort of a programmer on a project (measured by person months worked) Some Other Software Measures Programmer productivity Module defect density Defect detection efficiency Requirements stability Test effectiveness ratio System spoilage LOC produced person months of effort number of defects module size number of defects detected total number of defects numb of initial requirements total number of requirements number of items covered total number of items effort spent fixing faults total project effort 10

11 For More Information about Tools For Java programs in Eclipse cs For other tools see Engineering/toolcat.html#label210 11