Computer Technology Institute. Patras, Greece. In this paper we present a user{friendly framework and a

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1 MEASURING SOFTWARE COMPLEXITY USING SOFTWARE METRICS 1 2 Xenos M., Tsalidis C., Christodoulakis D. Computer Technology Institute Patras, Greece In this paper we present a user{friendly framework and a methodology to analyse and interpret the extracted results, while applying software metrics. This methodology could be used for fast and ecient software quality control, without being an expert, regarding the metrics used and the particular software. 1 INTRODUCTION Nowadays a high number of software projects fails to follow their prespecied requirements regarding time, budget and specications. Also their maintenance eort is higher than their implementation eort. Thus, there is a great need for software metrics, in order to aid towards the overcoming of this \software crisis". Though, the results of the software metrics are not used eciently, in order to direct those actions which will lead towards the improvement of the software's quality. This is because the metric's results are not fully analysed and interpreted. In order to preform measurements we use the ATHENA metrics environment, which has already been presented in a former paper /7/. The overall architecture of ATHENA is depicted in gure 1. When measuring a specic programming language the grammar processor (gp) processes its specications described in the language specication le (LSF) and accordingly produces a metrics processor (mp) for this language. Then the metric processor processes source les and produces the RESULTS le, 1 This paper was supported in part by QUICHE Project in the AIM framework 2 Proceedings of the 3rd European Conference on Software Quality, Sponsored by EOQ (European Organization for Quality), Madrid Spain, pp , 1992.

2 RP report GP MP RES GXP graphics LSF source DBP data-base The ATHENA metrics environment Figure 1: The ATHENA metrics environment holding the results of all measurements. The graphics processor (gxp) processes the RESULT le in order to present the results graphically. The report processor (rp) process the RESULT le and generates a report text le. Finally, the database processor (dbp) saves the RESULTS le in a database. The main goal of this paper is to demonstrate how the extracted from ATHENA measurement results could be analysed and interpreted 3.It provides a methodology, used to evaluate software and locate its problem areas. Section 2 includes a brief presentation of the metrics, which were used in this methodology. Section 3 contains the analysis of the metrics, some ways towards interpretation of the results and the methodology. Finally, in section 4, future goals and open problems are briey discussed. 2 PRESENTATION OF METRICS 3 A tool to preform the methodology has also been developed

3 Although ATHENA is a metrics{free 4 environment, in our methodology we use three well{known metrics. These are Halstead's \Software Complexity Measure" /4/, McCabe's \Cyclomatic Complexity Metric" /5/ and Tsai's \Data Structure Complexity Metric" /6/. A brief presentation follows. 2.1 Software Complexity Measure Halstead considers a program as a collection of tokens (operators or operands). The measurements of the program are based on counts of those tokens. Four basic quantities are proposed: n 1 : the number of distinct operators. n 2 : the number of distinct operands. N 1 : the total occurrences of operators. N 2 : the total occurrences of operands. Halstead proposed some additional quantities. Those quantities presented in table 1 are used for the interpretation and the methodology of this paper. Quantity's Name Evaluation Formula Program's Length N = N 1 + N 2 Length's Estimator ^N = n1 log 2 n 1 + n 2 log 2 n 2 Language's Level = N(log 2 n)(2n 2 =n 1 N 2 ) 2 Eort E = Nn 1 N 2 (log 2 n)=(2n 2 ) Time Estimation T = E=S where S 18 Table 1: Halstead's Quantities 2.2 Cyclomatic Complexity Metric 4 We can use any new metric, providing that we will formally describe it

4 sequent. while-do do-while switch if-then-else Various McCabe subgraphs Figure 2: McCabe's statement cases Cyclomatic complexity metric, proposed by McCabe, is used to measure the complexity of a program, as implied by its name. Those measurements take place on the control graph of the program. The cases of the possible statements into a control graph are shown as follows: sequentially executed statements, a While Do statement, a Do While statement, a Switch statement, and an If Then Else statement. All the other commands could derive from the above. The graphs 5 for each one of them are shown at gure 2. McCabe dened the cyclomatic number of a graph as: V (G) = e; n+2p where: e is the number of edges, 5 The control graph for the switch statement is based on our experience using metrics

5 n is the numberofnodes,and p is the number of connecting components. Additionally McCabe dene the essential complexity as: ev = V (g) ; m where m is the number of subgraphs of the control graph, which have a unique input and output node. 2.3 Data Structure Complexity Metric This metric proposed by Tsai counts the complexity of the data structures, used by the program and, therefore, is applicable before the program's implementation. To apply this metric, the measured software must uses data structures D of the form: D :: F(D 1 ::: D n ), with n 1 where F is the operator 6 which builds the data structure. D i are references into previously dened data structures, which are used to build the structure D while n is their number. Tsai's metric takes as input a series of data structures Q of the form: Q = fd i jd i :: F j (D s1 ::: D sk )g, with 1 k(i) where F j are the data structure buildoperators and D s x (1 x k(i)) are references into previously dened data structures. First of all, a multigraph 7, which shows the references into the structure denitions is built. Afterwards, the graph is reduced, by deleting the nodes with unique input and output. Then the strongly connected components are located and a monomial for each one of them is dened as: S(K)=(V + E)x L 6 Internal function 7 In order to allow circular denitions

6 S=4x S=6x^2 S=6x^4 Various Tsai multigraphs Figure 3: Tsai's example graphs where V is the number of nodes of the component, E is the number of edges and L is the number of simple circular paths into this component. We dene an ordering relation as follows: If K i 6= K j, then retains K i K j If and only if there is a path from some node of K j to a node of K i It can be easily proven that is a partial ordering. There is a non empty set M: M = fk i j not exist K j : K j K i g Now, for each component, a polynomial of its complexity can be calculated as follows: S(K)= X 8KiK fc(k i )g + S(K) This polynomial expresses the complexity of each data structure used by the program. Examples of graphs and their complexity are illustrated in gure 3. 3 INTERPRETATION OF METRICS AND

7 PRESENTATION OF THE METHODOLOGY Before presenting the methodology, is important to interpret the metric results. In the following section we will combine all the partial interpretations, in order to evaluate software's quality. A brief presentation of the interpretation of the most signicant quantities follows. n 1 : The number of unique operators is limited (depending upon the programming language). It tends to stabilise its value while the program's length grows. Since each Goto label is a unique operator, the value of n1, when exceeds the average, in most cases, means non{structured programming with low quality. N and ^N : N counts the actual program's length. On the other hand, ^N ignores any code impurities. Thus, correlation of them indicates a program without code impurities. Actually, things are not so simple, because this correlation is eected by the program's size. Christensen's /1/ measurements shown the best correlation for modules between 2000 to 4000 tokens. Additionally, our measurements indicate the best correlation for subroutines, ranging between 300 to 800 tokens. : The language's level () is expected to be similar for every program written in the same language. But that does not occur. In fact, it depends upon the use of the language by the individual programmer. Existing estimated averages for, in every commonly used language, comparable to each resulting, for every measured program, could give an evaluation of \how well is the language used". Eort and Time : Funami's /3/ measurements indicate a 98% correlation between the program's eort and the number of bugs, as this was measured during the program's testing phase. Additionally, our experiments shown great correlation between estimated and real time.

8 V (g) and ev : As V(g) grows, the program's complexity grows and in most cases its quality decreases. Measurements, performed by McCabe show the number 10 as an upper limit for the cyclomatic complexity of a software's part. A program with V (g) >> 10 its better to get restructured, in order to become less complex. Every program's cyclomatic complexity could be reduced 8 until V(g)=ev. Additionally, could be proven, that the essential complexity of every structured program is equal to one. This means that structured programs could be reduced down to any cyclomatic complexity we have chosen (having minimum one). But, for the non{structured programs, we have the essential complexity as an indicator, to show their reducing ability down to an accepted value. Tsai's polynomials : In the previous section, metric results are polynomials of the form: a 0 + a 1 x + :::+ a n x n. By examining the data structure types that can occur in it, a program is distinguished, according to its data structure types, into two basic categories: a) Non{circular data structures : These are data structures which do not consist of recursive (circular) data denitions. b) Circular data structures : These are recursively dened data structures, which denition contains circular references. In Tsai's metric, exponents measure complexity due to circular denitions, and coecients measure complexity due to non{circular denitions. 3.1 The Methodology An expert scientist could view the tables, which consist of a program's metric results and evaluate its quality. But it uses his experience, rather than an automated method. To develop a program, which will preform the 8 The value of ev shows us the best complexity which could be achieved by breaking the program down to subroutines.

9 MODULE measurements analysis routine 1 Halstead results routine 2 McCabe results Analysis routine N OUTPUT Tsai results The measuring process Figure 4: The measuring process same task, was very dicult. There are many dierent cases to be tested, resulting a very complex program. In this section we will present the main idea of the used methodology, without entering in technical details. The measuring process is illustrated in gure 4. It shows how the three used metrics are applied and how their results are collected for each module. Halstead's and McCabe's metrics are applied in every routine and Tsai's metric in every module. Then, an evaluation of the module's quality takes place. This process is repeated for every module. The overall results are statistically analysed, primarily routine by routine and then module by module. Thus, a conclusion about the overall program's quality could be drawn and additionally its problematic modules and routines could be located. The analysis is aided by statistics stored in the database, from previous measurements of other programs. ATHENA environment aided the automation of the methodology, providing the necessary tools. An outlined presentation of this methodology is given in the

10 following steps: Step 1: Apply Halstead's and McCabe's metrics on routine. Store results into database. Step 2: Step 3: Step 4: Repeat step 1 until the end of module. Apply Tsai's metric on module. Store results into database. Repeat steps 1{3 until the end of project. Step 5: Statistically analyse results for every routine. Store results into database. Step 6: Statistically analyse results for every module. Store results into database. Step 7: Interpret the stored results using the output from the statistical analysis. Step 8: Cross-examine the results with those from other projects. Step 9: Conclude about project's quality (using results from steps 5{8). Locate problematic routines and modules. At table 2, the questions asked on every module are presented. Of course, the actual questions are not so simple. The rst 6 questions deal with routine's evaluation and the last one with module's evaluation. The order of the rst six represents the sequence of the automation and the way their results contribute in the nal result. The analysis of the answers, based on the previously presented interpretations, is also not so simple. For instance, when asking \are N and ^N correlated?" you don't answer \Yes" or \No", but a number showing the degree of their correlation. Based on the above analysis, this degree is higher for the pair (N = 25 and ^N =50, with ^N =2N), than the pair (N = 600 and ^N = 800, with ^N = 2 3 N).

11 # Quantity's Name Analysis 1 N is between 300 to 800? 2 V(g) and ev V (g) 10 and ev =1? 3 n 1 is near the average? 4 Nand ^N are correlated? 5 is near the expected one? 6 E and T are in low levels? 7 Tsai's polynomials are simple or complex? Table 2: Simple questions 4 CONCLUSION We have presented three metrics, their interpretation, a methodology to evaluate the quality of software and the environment under which this methodology was automated. The presentation of the methodology has been basically outlined, in order to maintain the paper's size in low levels. Including more metrics in it, is a future goal. To make the automated program simpler is another future goal and is already in progress. REFERENCES /1/ Christensen K., Fitsos G., Smith C. \A Perspective on Software Science" IBM SYST J. Vol. 20. No 4. (1981) /2/ Christodoulakis D., Tsalidis C. \Design Principles of the ATHENA Software Maintainability Tool" Microprocessing and Microprogramming, The Euromicro Journal, Vol. 28. No. 1{5, (1990) /3/ Funami Y., Halstead M. \A Software Physics Analysis of Akiyama's Dubugging Data" Symposium on Computer Software Engineering Polytechnic Institute of New York, (1976) /4/ Halstead M. \Elements of Software Science" Elsevier, New York, (1977)

12 /5/ McCabe T. \A complexity Measure", IEEE Transactions on Software Engineering, Vol. SE{2, No 4, pp. 308{320, (1976) /6/ Tsai W., Lopez M., Rodriguez W., Volonik D. \An Approach to Measuring Data Structure Complexity", IEEE COMPSAC 86, pp. 240{246 (1986) /7/ Tsalidis C., Christodoulakis D., Maritsas D. \ATHENA: A Software Measurement and Metrics Environment" Software Maintenance: Research and Practice (1991)