Measuring Reuse


Jeffrey S. Poulin


IBM Corporation

P. O. Box 950, A80D/996, Poughkeepsie, NY 12602

Tel: (914) 432-1516, fax: (914) 432-3601



26 October 1992



This position paper describes a framework for measuring software reuse.  The method relies on readily available software data elements and defines three metrics derived from the observable data. These metrics may be used to assess the success of an organizational reuse program by quantifying the reuse practiced in the organization and estimating the resulting financial benefit.


The key to any reuse metric is the accurate reflection of effort saved.  The author developed and implemented a definition of reuse that distinguishes the savings and benefits from those already gained through accepted software engineering techniques.


Keywords: Reuse Metrics, Measuring Reuse, Software metrics, Return on Investment Analysis.


Workshop Goals: Learn and exchange information on reuse methods and metrics.


Working Groups: Design guidelines for reuse, Useful and collectible metrics, Reuse and formal methods.


1          Background

The author has been active in the area of software reuse since 1985 and was key to the development and acceptance of the IBM software reuse metrics.  He has conducted extensive research in software measurement techniques and implemented a program measurement tool for the workstation platform.  He is the lead technical member of the IBM Reuse Technology Support Center (RTSC) with responsibility for IBM reuse standards and metrics.


2          Position

2.1          Motivation

Software metrics are an important ingredient in effective software management.  Metrics are a means to measure “the software product and the process by which it is developed’’[Mills88].  The metrics can then be used to estimate costs, cost savings, or the value of a particular software practice.


As with general software metrics, reuse metrics must quantify the effect of the software process and the benefit it provides.  However, to assist in the technology insertion process, reuse metrics must also encourage the practice of reuse.  Since reuse is bi-directional (reusing software and contributing reusable software), reuse metrics must recognize both activities. Finally, the metrics must establish an effective standard that may be implemented by the development organizations in the enterprise.


Central to improving the practice of reuse is the understanding that good design and management is common within development organizations, but is less common between organizations.  Communication, which is necessary for the simple exchange of information and critical to sharing software, becomes more difficult as the number of people involved grows and natural organizational boundaries emerge.  Therefore, measurements must encourage reuse across these organizational boundaries.


A software component is reused when it is used by an organization that did not develop or maintain the component.  Software development organizations vary, but for measuring reuse a typical organization is either a programming team, department, or functional group of about eight people.  Also, although organizational size is a good indicator of how well communication between organizations takes place, functional boundaries are equally important.  For example, a small programming team may qualify as an organization if it works independently.


Our experience in IBM is that establishing a realistic return on investment on a reuse program is essential to inserting reuse into a corporate software development process.  Clearly stating the potential benefits of reuse in financial terms has proven to be a powerful motivator.  However, the business case given by the return on investment model must be achievable and not just demonstrate the substantial benefits of reuse.  This position paper describes the reuse metrics and return on investment process in place at IBM.

2.2        Observable Data

Reuse metrics are composite representations of the following observable data elements.  Note that alternatives to “lines of code’’ for measurement are equally effective (for example, function points[Banker91a] [Dreger89]. 



It is absolutely essential when acquiring the observable data elements, especially RSI, to recognize when reuse actually saves effort.  This requires the researcher to distinguish reuse from normal software engineering practices (e.g., structured programming) and to eliminate implementation-dependent options effecting the observable data elements. (e.g., static versus dynamic subprogram expansion).  [Poulin92] provides a detailed approach to these considerations.

2.3        Derived Data

The observable data elements combine to form three derived reuse metrics.  These are:[Poulin92]


  1. Reuse Percentage; the primary indicator of the amount of reuse in a product.  Derived from SLOC and RSI [Banker91b].
  2. Reuse Cost Avoidance; indicator of reduced total product costs as a result of reuse in the product.  Derived from SLOC, RSI, error rates, and software development and maintenance costs [Gaffney89] [NATO91].
  3. Reuse Value Added; an indicator of leverage provided by practicing reuse and contributing to the reuse practiced by others.  Derived from SLOC, RSI, and SIRBO [Hayes89] [Margano91].


The purpose of the Reuse Percentage measurement is to indicate the portion of a product, product release, or organizational effort that can be attributed to reuse.  Reuse Percentage is an important metric because it is simple to calculate and it is easy to understand.  Unfortunately, it is also easy to misrepresent without a supporting framework. Many companies report their reuse experiences in terms of “reuse percent,” but few describe how they calculate the values. They commonly include informal reuse in the value, making it difficult to assess actual savings or productivity gains.  Since RSI is clearly defined, the reuse percentage metric is a reasonable reflection of effort saved.


The purpose of the Reuse Cost Avoidance (RCA) measurement is to quantify the financial benefit of reuse.  In addition to historical development data, RCA is based on a “relative cost of reuse,” and incorporates the effort to search for, retrieve, and assess the suitability of reusable components for integration into a product.  RCA is a particularly important metric because it shows the tremendous return on investment potential of reuse.  Because RCA is a key metric in performing return on investment (ROI) analysis of reuse programs, RCA also helps with the insertion of reuse technology.


The previous two metrics measure how much organizations reuse software.  It is also important to motivate contributing software to reuse.  The purpose of the Reuse Value Added is to provide a metric that reflects positively on organizations that both reuse software and help other organizations by developing reusable code.  RVA is a ratio, or productivity index; organizations with no involvement in reuse have an RVA=1.  An RVA=2 indicates the organization is twice as effective as it would be without reuse. In this case the organization was able to double its productivity either directly (by reusing software) or indirectly (by maintaining software that other organizations are using).


3          Comparison

There are other reports on reuse measurements available.  Although reuse percent is most common metric, the majority of published methods focus on financial analysis.  This is because the cost benefit of reuse is a highly convincing measure for project managers and planners.  The three derived metrics are related to their corresponding references in the previous section.  The definition and the collection of the observable data is ingrained in the IBM programming process; the application of the observable data items is explained in [Poulin92].


Of the several measurement methods currently in use, the method in this paper is unique in the attention given to the definition of RSI and in attempting to present reuse as “real effort saved.” Although [Banker91a] differentiates between reuse within an organization and reuse from sources external to the organization, no other paper addresses the distinction between software engineering techniques and reuse, nor do they provide a concentrated definition of RSI.


4          Bibliography

[Banker91a] Rajiv D. Banker and Robert J. Kauffman, “Reuse and Productivity in an Integrated Computer Aided Software Engineering (ICASE) Environment: An Empirical Study at the First Boston Corporation”, First Boston Corporation, 10 July 1991.


[Banker91b] Rajiv D. Banker, Robert J. Kauffman, Charles Wright and Dani Zweig, “Automating Output Size and Reusability Metrics in an Object-Based Computer Aided Software Engineering (CASE) Environment,” First Boston Corporation, 25 August 1991.


[Mills88] E.E. Mills, “Software Metrics,” SEI Technical Report SEI-CM-12-1.1, 1988.


[Dreger89] Dreger, J.B. Function Point Analysis. Prentice-Hall, 1989.


 [Gaffney89] Gaffney Jr., J.E. and Durek, T.A., “Software Reuse- Key to Enhanced Productivity: Some Quantitative Models,” Information and Software Technology, Vol. 31, No. 5, June 1989.


[Hayes89] Hayes, W.E., “Measuring Software Reuse,” International Business Machines, IBM Document Number WEH-89001-2, 2 October 1989.


[Jones91] Jones, Capers. Applied Software Measurement: Assuring Productivity and Quality.  McGraw-Hill, 1991.


[Margano91] Margano, Johan and Lynn Lindsey, “Software Reuse in the Air Traffic Control Advanced Automation System,” Joint Symposia and Workshops: Improving the Software Process and Competitive Position, Alexandria, VA, 29 April-3 May 1991.


[NATO91] NATO, “Standard for Management of a Reusable Software Component Library,” NATO Communications and Information Systems Agency, 18 August 1991.


[Poulin92] Poulin, Jeffrey S. and W.E. Hayes, “IBM Reuse Methodology: Measurement Standards,” International Business Machines Internal Document, 16 July 1992.


5          Biography

Jeffrey S. Poulin is an advisory programmer at IBM’s Reuse Technology Support Center, Poughkeepsie, New York.  His primary responsibilities include corporate standards for reusable component classification, certification, and measurements.  His interests include object-oriented database systems, semantic data modelling, CASE, and formal methods in software reuse.  He is currently conducting research into alternative methods for reusable software distribution and retrieval.  He is a member of the IBM Corporate Reuse Council, the Association for Computing Machinery, and Vice-Chairman of the Mid-Hudson Valley Chapter of the IEEE Computer Society.  He received his Bachelor’s degree from the United States Military Academy at West Point, New York, and his Master’s and Doctorate degrees from Rensselaer Polytechnic Institute in Troy, New York.