Software Maintenance Productivity Research Articles

The impact of UML modeling on defect density and defect resolution time in a proprietary system

Background: The contribution of modeling in software development has been a subject of debates. The proponents of model-driven development argue that a big upfront modeling requires substantial investment, but it will payoff later in the implementation phase in terms of increased productivity and quality. Other software engineers perceive modeling activity as a waste of time and money without any real contribution to the final software product. Considering present advancement of model-based software development in software industry, we are challenged to investigate the real contribution of modeling in software development. Objective: We analyze the impacts of UML modeling, specifically the production of class and sequence diagrams, on the quality of the code, as measured by defect density, and on defect resolution time. Method: Using data of a proprietary system, we conduct post-mortem analyses to test the difference in defect density between software modules that are modeled and not modeled. Similarly, we test the difference in resolution time between defects that are related to modeled and not modeled functionality. Result: We have found that the production of UML class diagrams and sequence diagrams reduces defect density in the code and the time required to fix defects. These results are obtained after controlling for the effects of co-factors such as code coupling and complexity. Conclusion: The results confirm that not only does the production of UML class diagrams and sequence diagrams possibly help improve the quality of software, but also it possibly help increase the productivity in software maintenance.

Building Productivity Models for Small Enhancements

Software is in constant evolution and many approaches have been suggested to study software maintenance productivity. This research reports on a process to design and implement a productivity model of legacy software based on the measurement of small functional enhancements using the COSMIC ISO 19761 international standard. Two motivations influence this research: 1) understanding the productivity of the software maintenance process to help manage the cost of maintenance; 2) understanding the cost drivers that affect the software maintenance productivity. This research reports on an empirical study of a productivity measurement program implemented in a large banking legacy system.

Software maintenance productivity assessment using fuzzy logic

This paper proposes a fuzzy logic based precise approach to quantify maintenance productivity of software. Cyclomatic complexity density i.e., cyclomatic complexity per lines of code is proposed as a metric for software maintenance productivity. Triangular fuzzy numbers are used to represent cyclomatic complexity density. Fuzzy logic offers significant advantages over other approaches due to its ability to naturally represent qualitative aspect of inspection data and apply flexible inference rules based on fuzziness. The model is evaluated on the basis of published data for a small pilot project on actual maintenance data. However, the technique is quite general and may be tested for medium and large projects in other languages. Results obtained using fuzzy logic is better than results obtained by existing technique without fuzzy logic [9].

A Field Study of Scale Economies in Software Maintenance

Software maintenance is a major concern for organizations. Productivity gains in software maintenance can enable redeployment of Information Systems resources to other activities. Thus, it is important to understand how software maintenance productivity can be improved. In this study, we investigate the relationship between project size and software maintenance productivity. We explore scale economies in software maintenance by examining a number of software enhancement projects at a large financial services organization. We use Data Envelopment Analysis (DEA) to estimate the functional relationship between maintenance inputs and outputs and employ DEA-based statistical tests to evaluate returns to scale for the projects. Our results indicate the presence of significant scale economies in software maintenance, and are robust to a number of sensitivity checks. For our sample of projects, there is the potential to reduce software maintenance costs 36% by batching smaller modification projects into larger planned releases. We conclude by rationalizing why the software managers at our research site do not take advantage of scale economies in software maintenance. Our analysis considers the opportunity costs of delaying projects to batch them into larger size projects as a potential explanation for the managers' behavior.

Cyclomatic complexity density and software maintenance productivity

A study of the relationship between the cyclomatic complexity metric (T. McCabe, 1976) and software maintenance productivity, given that a metric that measures complexity should prove to be a useful predictor of maintenance costs, is reported. The cyclomatic complexity metric is a measure of the maximum number of linearly independent circuits in a program control graph. The current research validates previously raised concerns about the metric on a new data set. However, a simple transformation of the metric is investigated whereby the cyclomatic complexity is divided by the size of the system in source statements. thereby determining a complexity density ratio. This complexity density ratio is demonstrated to be a useful predictor of software maintenance productivity on a small pilot sample of maintenance projects. >

A Model to Evaluate Variables Impacting the Productivity of Software Maintenance Projects

The cost of maintaining application software has been rapidly escalating, and is currently estimated to comprise from 50–80% of corporate information systems department budgets. In this research we develop an estimable production frontier model of software maintenance, using a new methodology that allows the simultaneous estimation of both the production frontier and the effects of several productivity factors. Our model allows deviations on both sides of the estimated frontier to reflect the impact of both production inefficiencies and random effects such as measurement errors. The model is then estimated using an empirical dataset of 65 software maintenance projects from a large commercial bank. The insights obtained from the estimation results are found to be quite consistent for reasonable variations in the specification of the model. Estimates of the marginal impacts of all of the included productivity factors are obtained to aid managers in improving productivity in software maintenance.