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GQM++ A Full Life Cycle Framework for the Development and

GQM++ A Full Life Cycle Framework for the Development and
GQM++ A Full Life Cycle Framework for the Development and

Canberra, Australia, ASMA (1997) 22-35

GQM++ A Full Life Cycle Framework for the Development and Implementation of

Software Metric Programs

Andrew Gray and Stephen G. MacDonell

Software Metrics Research Laboratory

Department of Information Science

University of Otago

Dunedin, New Zealand

Abstract

One of the more challenging aspects concerning the development of a sound and maturing software process is the determination of an appropriate and relevant supporting measurement program. At one time process and product measurement was a fantasy, something that was undertaken by other organisations. With the advent of process improvement models, metric program development has now gained extensive support. Moreover, the use of frameworks such as the Goal/Question/Metric (GQM) approach has enabled managers to more effectively and confidently select candidate metrics or measures. It is our contention however, that GQM only takes us some way to the development of a feasible program, and that there are other considerations that should be made before embarking on organisation-wide collection schemes. These considerations include in particular an explicit acknowledgment of the costs and benefits of data collection, the specification of potential modelling and analysis methods, and the determination of how any results might be ‘fed back’ into the process. We therefore propose an extension to the GQM framework, which we have called GQM++, to enable the more formal consideration of such issues. Although as yet untested, it is our opinion that explicit specification of these other metric program characteristics will result in the development of more comprehensive, pragmatic and feasible data collection and analysis processes.

Introduction

Modern software development has become increasingly complex and difficult to manage as the size and functionality of systems have grown and as increasing numbers of competing and co-operative development tools and methodologies have become available. Such circumstances make the use of software metrics even more crucial than in the past. Software metric models can be used to predict, classify or analyze the effects of aspects of the software development process on other aspects. For example, development effort may be predicted based on a set of size and complexity measures, modules may be classified into those suitable for reuse and those that are not, and a particular methodology may be analyzed in terms of its effect on developer productivity as compared to another methodology. Metrics can be used at various stages of the software process – before development as part of project planning, during development to monitor and control, and after development is completed to assess the project’s ‘success’.

One major difficulty associated with the adoption of a metrics program is in the determination of the particular metrics that should be collected. All data collection has associated costs, both direct (as in terms of people employed and software purchased) and indirect (as in the costs of interruptions and delays as metrics are analyzed). For this reason, a commercial metrics program should be treated as an investment in the development process, requiring justification for expenditure and providing some quantification of the resulting benefits. For researchers in the metrics field similar concerns arise – data collection still generally incurs costs and the metrics selected must be capable of producing the desired outcome. Organizations are faced with a wide range of software metrics in both the research literature and as promoted by metrics consultants. This can lead to difficulties in the selection of the appropriate metrics to collect for a particular organization with particular goals in mind. Given the uniqueness of each organization, the application of some generic set of metrics is unlikely to be as effective as a more tailored approach. Similarly within a research context, a custom set of metrics, possibly including metrics that have not been used before, will generally provide more meaningful results and at the very least should advance the discipline into new areas.

When developing a set of software metrics, whether the purpose is for the actual management of a commercial software development project or as part of a research program, it is important to ensure that the optimal set of metrics is chosen. By optimal it is not just meant that the metrics should be those that would provide the greatest predictive power for the desired purpose. It is also important that the cost of data collection is minimized (or balanced with the benefits to be gained from the accuracy of the model). The possibility for reusing metrics collected for other purposes should also be considered as part of the collection cost. The data collected should of course be useful from the perspective of enabling appropriate models to be developed. Even more important however, is that the metrics should provide information that will enable progress towards some improvement-oriented goal or goals. As can be seen, there are several separate dimensions to the suitability (or optimality) of a given set of metrics for a particular organization or research program.

In order to develop a set of metrics that is optimal in this sense it seems useful to work within a framework that will assist with the decomposition of goals into the metrics that should be collected, that will identify the costs and benefits of the program, and ensure that the entire process is indeed feasible. This paper outlines the currently most popular such approach to selecting suites of metrics with some goal in mind, the Goal/Question/Metric (GQM) framework, and attempts to resolve some of its weaknesses through additional stages. Since the GQM framework is already widely used, it is hoped that these incremental changes will be comparatively simple for many organizations and researchers to use where appropriate.

The Goal/Question/Metric Framework

One method for selecting a suite of metrics to be captured with a goal (or set of goals) in mind is the Goal/Question/Metric (GQM) framework. GQM was proposed by Basili and others (Basili and Weiss 1984; Basili and Rombach 1988) with the aim of providing a systematic approach to the determination of project goals that can be refined into questions that can in turn be answered in a quantifiable manner by certain software metrics (Figure 1, with an example in Figure 2). A goal is usually defined in terms of a particular purpose (e.g. to evaluate, predict, classify) from a certain perspective (e.g. the manager, customer, developer) for a given object (e.g. specification correctness, code modularity), within a particular environment (e.g. the people, tools, methodologies).

Figure 1 The GQM Framework

Figure 2 An Example of a GQM Framework

Thus, the effective application of GQM helps to ensure that the metrics collected will indeed be useful for the purposes of achieving certain goals. It also assists with the conceptual understanding of those goals and metrics by providing a hierarchical framework within which the goal(s) can be broken down into questions, that then in turn determine the actual metrics.

GQM has been extended from this basic model by several authors. Extensions include those shown in MacDonell (1993) where the basic framework is extended into a hierarchy of goals, subgoals, domains, subdomains, questions, subquestions, characteristic measures, and finally measures at two separate levels appropriate to his domain of CASE and 4GL effort prediction (Figure 3, with an example adapted from MacDonell in Figure 4). While these extensions of GQM apparently reduce some of the simplicity and ease of use inherent in the original technique, it is felt that as long as these extensions are seen as optional, and therefore only used when necessary, they actually reduce the complexity of the hierarchy by enabling a more consistent and systematic breakdown of the components. For example, by breaking a goal into separate sub-goals the essential differences between metrics needed for each sub-goal can be identified, while still identifying the common measures used for both (Figure 5). This may be important if only one sub-goal is ultimately pursued, and assists with matching the costs and benefits of parts of a metrics program.

Figure 3 The Extended GQM Framework

Figure 4 An Example of the Extended GQM Framework (Based on MacDonell, 1993)

Figure 5 Common Versus Single-Use Measures in a GQM Framework

However, such extensions do not seem to go far enough. One criticism of GQM is that while it is useful for identifying objectives for measurement, it does not address the actual problems of measurement (Fenton and Pfleeger 1997). While Fenton’s focus is more on the technical aspects of measurement, such as appropriate data scales, the criticism can be extended to the absence of any feasibility, economic, or correctness checks in GQM. It is not merely that GQM does not enforce these checks, but in its simple and intuitive nature, it can actually lead to these problems, as Fenton points out with respect to problems of measurement.

Other Issues in Developing a Suite of Software Metrics

While GQM provides an efficient means of selecting software metrics based on organizational goal(s), many other factors need to also be considered. These include the cost of data collection, the expected benefits of the metrics program, the type of modeling to be used, and the manner in which the program will affect the software development process (see also Card (1993) and Roche et al. (1996)).

Cost of Data Collection

In order to assess the actual worth of a metrics program it is first necessary to determine and associate the costs involved with each measurement recorded. While allocating costs at the individual measurement level may seem unnecessary, it allows for a more accurate matching of measurement costs to each particular goal or sub-goal. In the case of measurements where costs are not separable, some equitable division (usually shared evenly) can be used.

Costs should be estimated initially as with any investment, and compared with the expected revenues (as discussed in the following section). The actual costs should be monitored as they are incurred, allowing for corrective action if the estimates appear to be incorrect. Expected Benefits of the Metrics Program

The benefits of a metrics program are much more difficult to quantify than the costs. Despite this, some form of estimation is important, and while the values may be g uesstimates this should not be problematic, providing this fact is acknowledged. Since software metrics are similar to security and insurance in the sense that they are often regarded as only possibly necessary, until their absence incurs some substantial cost, this is a good opportunity for those involved in metrics to list the benefits that their services provide. While the aim of a metrics program will usually be to achieve some particular goal, it remains necessary to ensure that the benefits of achieving that goal will indeed surpass the costs.

Modeling Techniques

When developing a model using software metrics a large number of techniques are available. These include the more traditional regression and ANOVA approaches as well as some more recently adopted techniques such as neural networks, fuzzy logic, case-based reasoning, and more advanced statistical techniques (clustering, factor analysis, and robust estimators). Given the fact that each technique has its own requirements in terms of, for example, the size of the data set and the distribution assumptions of the data, it is important that the correct technique is selected. In many cases, a single technique may not be sufficient and several models may be developed simultaneously. It is important that all of the techniques chosen will remain effective under the data and operating conditions.

Each type of technique also provides different benefits. The worth of a technique depends on the accuracy required and the desired amount of interpretability in the final model. While it may appear trite, it is also important that an organization has available the required expertise to implement the model(s). It is often the case that the more advanced modeling paradigms,

such as neural networks, are adopted on the (unfounded) assumption that they are inherently better, even when the expertise to use them correctly is not available. Gray and MacDonell (1997) provide a summary of the worth of various techniques for software metric applications and MacDonell and Gray (1996a) illustrate the effectiveness of some techniques on software metrics data.

Effect of Metrics on Software Development

Once the models have been developed it is essential that the benefits are then realized. Quantifying the potential benefits, as discussed above, and actually achieving these benefits are sufficiently different that the implementation of the metric models is regarded here as deserving special attention. A number of qualities of successful metrics programs are discussed in MacDonell and Gray (1996b):

?user involvement

?management support

?clear requirements

?proper planning

?realistic expectations

?smaller project milestones.

The effective implementation of the software metrics program will require all of these characteristics. As user involvement and management support are essential components of a successful metrics program, all stakeholders should be involved in determining which metrics should be collected and how they should be used. In other words, the use of a technique like GQM cannot (or at least should not) be carried out by project managers in isolation from the rest of the organization.

The implementation plan for the program should cover such issues as how the models will be used to plan, monitor, control, or evaluate the development process. The allocation of responsibilities for who will interpret the models’ results is also important.

GQM++

We propose here an extension to GQM that adds a further three layers to the model proposed in MacDonell (1993), for data collection, modeling, and implementation. The extension also captures cost/benefit information and assesses the program in terms of economic justification and feasibility. These additional layers, while involving actions to be taken in phases subsequent to planning, are important to consider as early as possible since they involve the creation of a series of steps that will achieve the goals of the metrics program. If some of these steps are not feasible, due to measures not being available, costs of gathering those measurements exceeding some budget, or expertise with the analysis techniques being outside the model developer’s range of skills, then this is the time to make adjustments to the program. The sooner that the entire project is planned in a feasible manner the lower the costs of plans committed to but found impossible. These additional layers should therefore be filled in tentatively, not as contracts to be fulfilled. Changes can be made at any stage, but the metrics program as a whole must remain feasible in technical, economic and managerial senses.

The additional layers incorporate the data collection method, the data analysis method, and finally the method used for the model’s implementation into the organization’s future software development processes. Thus, the extended model could be referred to as the Goal/Question/Metric/Collection/Analysis/Implementation (GQMCAI) framework or more simply as GQM++. Further extensions as used in Shepperd (1990) and MacDonell (1993) for sub-goals, questions, and metrics are encouraged and discussed here. This leads to a total of up to eleven layers, although some of the sub-layers may not be necessary for all problems. Model stages are discussed below, and are shown in Figure 6 with an example in Figure 7.

Goal

The goal is a broad level specification of what the program hopes to achieve. For example, a goal may be to enhance productivity of developers. Such a goal needs to be consistent with the organization as a whole. Goals for commercial organizations seeking an improvement in their development process should not be specified in vague terms, such as to improve productivity, although such goals may be suitable for research studies. Instead, such organizations should use precise, albeit flexible, goals such as to enhance developer productivity by five to ten percent over the next two years. These details could be contained in a sub-goal as described below.

A goal of this nature is both visible (which makes achievement more likely) and measurable (which allows for recognition of how well the goal was in fact achieved). Some assessment of the benefits of achieving the goal is also important. While quantifying the benefits from, for example, a five-percent increase in developer productivity as measured in function points is very difficult, it is none-the-less essential that some estimation is made. If the uncertainty attached to the estimate is recognized, a realistic comparison between the expected benefits and actual benefits can be performed. Capital budgeting techniques, such as net present values and sensitivity analysis, can be used to assess the overall profitability of the program. Sub-goal

This is an optional step mentioned in Shepperd (1990) and MacDonell (1993) that can be used to break the goal, which in the above example is rather broad, into smaller, more manageable, sub-goals. For example, productivity measurement could be broken into measuring productivity for analysis and design, coding, and testing. Each of these would be a sub-goal, and while some overlap may exist between questions, metrics, collection procedures, analysis techniques, and implementation methods, for each of these goals it may be useful to treat each separately, while still providing some indication of commonalties. As with the goal above, the sub-goals for an organization seeking improvement of its development process should be realistic and clearly specified. Assessing the benefits of sub-goals can be accomplished, and in fact may be even easier to perform, in the same manner as for goals presented above. Domain

The domain provides the context for the sub-goal or goal above. This is another optional stage that allows different collection and analysis approaches to be taken for different types of system. So, as an example, a goal of improving testing productivity may be implemented differently for CASE-built systems than for systems constructed with a third-generation language such as C++.

Sub-domain

This optional stage enables the further refinement of the domain above. Following on from the testing productivity example, the C++ testing productivity improvement goal may be implemented differently for libraries that are intended to be reused than for specialized, one-off use routines.

Figure 6 The GQM++ Framework

Figure 7 An Example of a GQM++ Framework

This level in the framework provides a number of questions for each (sub-)goal. These questions are selected in such as way as to make their answers sufficient, and ideally also necessary, in order to achieve the goal. Continuing the above example, questions may be proposed along the lines of

1.How do tools (such as editors and debugging utilities) affect productivity?

2.What effect do fourth-generation languages and CASE tools have?

3.Are visual programming environments assisting productivity?

4.Does team size affect productivity?

5.What effect does training for developers have on their productivity?

Sub-question

These, if necessary, allow the questions to be further broken down. The second question above (fourth-generation languages and CASE tools) could then be broken into specific questions on the individual effects of fourth-generation languages and of CASE tools. This may appear somewhat simplistic, and unnecessary, since we could have skipped the higher step and gone straight to the two sub-questions. While this can be done, it is considered useful to be able to recognize that the effects of fourth-generation languages and CASE tools, in this example, are likely to share considerable overlap in metrics used, but will probably also differ in some important ways depending on the particular processes employed. Characteristic Metrics

This is the set of metrics that will be used to answer the (sub-)questions above. Examples for our scenario in terms of CASE productivity would be to measure the size of the project, its complexity, and the effort expended on it.

Sub-metrics

This is another optional step, used in Shepperd (1990) and MacDonell (1993), since we could just provide the metrics at the higher stage. However, a number of size metrics exist (the data model size as measured by the number of entities or by the number of attributes, or the process model size as measured by the number of levels in the data flow diagrams, for example). We may therefore wish to look at using more than one metric of each type to reduce problems with contaminated data.

Data Collection

This is one of the additional layers proposed here. The data collection layer should state the time at which the data will be available, the way in which it will be obtained and the personnel responsible for this, and finally the cost associated with collection. The importance of this layer is in forcing the program to only use information that will actually be available. The potential exists for a metrics program to be created, only to find that some of the data cannot be collected, or not in sufficient quantities. A possible decomposition here is to separate this into a temporal source (for example, after analysis and after coding) and then the source itself (such as specification documents and the source code). However since we will normally use the most recently produced data source, for example measures from the code will supersede any from the specification, this option is not explored further here.

This is where the analysis procedures to be used will be specified. Since the quantity and nature of data actually collected will largely determine which techniques are suited to the particular analysis planned, this layer is relatively more likely to change. Some options here are for regression-based modeling, neural networks, fuzzy logic models, and case-based reasoning. At this level, it is important that the feasibility of the technique is examined, in terms of the data that will be available, the required characteristics of the model, and the technical expertise available. Since many measures may be used in one model, there will generally be far fewer models than metrics even with considerable sharing of measures between models.

Implementation

This layer describes how the models obtained will be implemented in order to achieve the goal(s) stated. For example, the selection of tools to be used by programmers may be made after investigating the impact of each. At this stage the benefits expected from the metrics program should be quantified as accurately as possible in relation to the benefits of the goals already estimated. This allows for a check that the implementation does in fact correspond to the goals in terms of benefits achieved. Specifying benefits from each measure may not always be practical, especially where several compound measures will be used together (such as several defect-tracking measures). Again, as above the models may be used in combination or in a single-shot manner.

Assessment

While not a layer in the hierarchy itself, this stage can be seen as the single point into which the entire framework is fed. This provides the opportunity to combine the costs and benefits from the program and to determine the parts of the program that should be proceeded with and those that will require adaptation or abandonment. The GQM++ process should be seen as iterative, with the possibility of working back up the hierarchy if some lower level cannot be sufficiently specified.

Conclusions

In this paper, an extension of the GQM framework that takes into account the data collection, model building, implementation, and program assessment stages has been presented. While not all of these stages will be required, or will even be desirable, for all metrics programs it is felt that many organizations would benefit from a more holistic approach to their selection of software metrics. The GQM++ framework encourages the hierarchical specification of software metrics, with the involvement of all stakeholders used to assess the profitability and feasibility of the metrics program.

References

Basili, V.R. and Rombach, H.D. (1988), ‘The TAME Project: Towards Improvement-Oriented Software Environments’, IEEE Transactions on Software Engineering 14(6),pp. 758-773.

Basili, V.R. and Weiss, D.M. (1984), ‘A Methodology for Collecting Valid Software Engineering Data’, IEEE Transactions on Software Engineering 10(6),pp. 728-738.

Card, D. (1993), ‘What Makes for Effective Measurement?’, IEEE Software November, pp. 94-95.

Fenton, N.E. and Pfleeger, S.L. (1997), Software Metrics – A Rigorous and Practical Approach, Chapman and Hall, London.

Gray, A.R. and MacDonell, S.G. (1997) ‘A Comparison of Techniques for Developing Predictive Models of Software Metrics.’ Information and Software Technology 39(6), pp. 425-437.

MacDonell, S.G. (1993), ‘Deriving Relevant Functional Measures for Automated Development Projects’, Information and Software Technology 35(9), pp. 499-512.

MacDonell, S. G. and Gray, A. (1996a), ‘Alternatives to Regression Models for Estimating Software Projects’, Proceedings of IFPUG1996 Fall Conference, Dallas TX.

MacDonell, S.G. and Gray, A. (1996b), ‘Software Process Engineering for Measurement-Driven Software Quality Programs - Realism and Idealism’, Proceedings of ACOSM’96, Melbourne.

Roche, J.M., Jackson, M. and Shepperd, M. (1996), ‘Improvement of the Goal-Question-Metric Method’, in Proceedings of the Software Process Improvement Conference (SPI’96), Brighton UK.

Shepperd, M. (1990), ‘Design Metrics: An Empirical Analysis’, Software Engineering Journal, January, pp. 3-10.

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