Software Maintenance Costs Research Articles

Extracting classification rule of software diagnosis using modified MEPA

Defective software modules cause software failures, increase development and maintenance costs, and reduce customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. In real-world databases are highly susceptible to noisy, missing, and inconsistent data. Noise is a random error or variance in a measured variable [Han, J., & Kamber, M. (2001). Data Mining: Concepts and Techniques , San Francisco: Morgan Kaufmann Publishers]. When decision trees are built, many of the branches may reflect noisy or outlier data. Therefore, data preprocessing steps are very important. There are many methods for data preprocessing. Concept hierarchies are a form of data discretization that can use for data preprocessing. Data discretization has many advantages, such as data can be reduced and simplified. Using discrete features are usually more compact, shorter and more accurate than using continuous ones [Liu, H., Hussain, F., Tan, C.L., & Dash, M. (2002). Discretization: An enabling technique. Data Mining and Knowledge Discovery , 6(4), 393–423]. In this paper, we propose a modified minimize entropy principle approach and develop a modified MEPA system to partition the data, and then build the classification tree model. For verification, two NASA software projects KC2 and JM1 are applied to illustrate our proposed method. We establish a prototype system to discrete data from these projects. The error rate and number of rules show that the proposed approach is both better than other methods.

Development of a human information processing model for cognitive task analysis and design

High costs and long cycle times of software development are factors hindering the analysis and design of the complete cognitive aspects related to task performances. Reusable software has been proposed as a solution to the problem of high software development and maintenance costs. One of the paradigms for reusable software, object-oriented modelling, has emerged as an important method for specifying, creating and reusing software modules. Using Unified Modelling Language, this paper develops an object-oriented cognitive task analysis and design (OOCTAD) model based on human information-processing theory. First, human task performance-related cognitive aspects are classified into modules and logically linked together; secondly, cognitive aspects within these modules were further decomposed into packages and classes based on object-oriented technology. Finally, one case study was presented to compare and contrast different cognitive analysis models to illustrate the advantages of the OOCTAD model developed in the current research over the other five cognitive task analysis models. These reusable classes in the OOCATD model were shown to be reused to reduce software development costs and cycle times for cognitive task analysis and design. The current research should be of use to both developers and users of cognitively based tasks, systems and tools and researchers and practitioners in the broader discipline of cognitive tasks design and analysis.

Building Defect Prediction Models in Practice

Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. These models often use static measures obtained from source code, mainly size, coupling, cohesion, inheritance, and complexity measures, which have been associated with risk factors, such as defects and changes.

Towards requirements-driven autonomic systems design

Autonomic computing systems reduce software maintenance costs and management complexity by taking on the responsibility for their configuration, optimization, healing, and protection. These tasks are accomplished by switching at runtime to a different system behaviour - the one that is more efficient, more secure, more stable, etc. - while still fulfilling the main purpose of the system. Thus, identifying and analyzing alternative ways of how the main objectives of the system can be achieved and designing a system that supports all of these alternative behaviours is a promising way to develop autonomic systems. This paper proposes the use of requirements goal models as a foundation for such software development process and sketches a possible architecture for autonomic systems that can be built using the this approach.

SOFTWARE RELIABILITY ISSUES UNDER OPERATIONAL AND TESTING CONSTRAINTS

Software reliability plays an important role in assuring the quality of a software. To ensure software reliability, the software is tested thoroughly during the testing phase. The time invested in the testing phase or the optimal software release time depends on the level of reliability to be achieved. There are two different concepts related to software reliability, viz., testing reliability and operational reliability. In this paper, we compare both types of software reliabilities to determine the optimal testing time of the software so as to minimize the total expected software maintenance cost. We consider a software has a number of clusters of modules, each having a different number of errors and a different failure rate. A hyperexponential model is employed for analyzing software reliability growth. Parameter estimation using the maximum likelihood estimation technique is also discussed. Numerical illustrations are taken to explore the effect of various parameters on reliability and maintenance cost. It is noticed that the operational reliability concept should be adopted for the software testing time problem.

A Decision Model for Software Maintenance

In this paper we address the problem of increasing software maintenance costs in a custom software development environment, and develop a stochastic decision model for the maintenance of information systems. Based on this modeling framework, we derive an optimal decision rule for software systems maintenance, and present sensitivity analysis of the optimal policy. We illustrate an application of this model to a large telecommunications switching software system, and present sensitivity analysis of the optimal state for major upgrade derived from our model. Our modeling framework also allows for computing the expected time to perform major upgrade to software systems.

Reusability Techniques in Load-Flow Analysis Computer Program

This paper describes the implementation of reusability aspects in producing load-flow analysis software application. The reusability in the algorithm and codes are obtained by applying matrix partitioning approach for Newton method and sequential approach for Fast Decoupled method. The software is built by using component-based development and object oriented programming methodologies. These methodologies made the developed load-flow analysis software flexible for future enhancement. Adding and changing codes and algorithm of the software can be done without affecting much the existing codes and algorithms. By maximizing the reusability aspects, the cost and time of software maintenance for updating can be reduced.

A Generic Approach for Gas Turbine Adaptive Modeling

For gas turbine engine performance analysis, a variety of simulation tools is available. In order to minimize model development and software maintenance costs, generic gas turbine system simulation tools are required for new modeling tasks. Many modeling aspects remain engine specific however and still require large implementation efforts. One of those aspects is adaptive modeling. Therefore, an adaptive modeling functionality has been developed that can be implemented in a generic component-based gas turbine environment. A single component in a system modeling environment is able to turn any new or existing model into an adaptive model without extra coding. The concept has been demonstrated in the GSP gas turbine modeling environment. An object-oriented architecture allows automatic addition of the necessary equations for the adaptation to measurement values. Using the adaptive modeling component, the user can preconfigure the adaptive model and quickly optimize gas path diagnostics capability using experimentation with field data. The resulting adaptive model can be used by maintenance engineers for diagnostics. In this paper the integration of the adaptive modeling function into a system modeling environment is described. Results of a case study on a large turbofan engine application are presented.

Software and the future of programming languages.

Although software is the key enabler of the global information infrastructure, the amount and extent of software in use in the world today are not widely understood, nor are the programming languages and paradigms that have been used to create the software. The vast size of the embedded base of existing software and the increasing costs of software maintenance, poor security, and limited functionality are posing significant challenges for the software R&D community.

Runtime software architecture based on reflective middleware

There exists a consensus that software architecture (SA) plays a central role in software development and also plays an important role in the lifecycle phases after software delivery. Particularly, SA can be used to reduce the great difficulty and cost of software maintenance and evolution. In this paper, runtime software architecture (RSA) based on reflective middleware is proposed to support architecture-based software maintenance and evolution. In this approach, the actual states and behaviors of the runtime system can be observed and manipulated in a consistent and understandable way through its architectural view. Being an accurate, up-to-date, semantic and operable view of SA, RSA looks components and connectors as “white-box” entities to accurately and thoroughly describe the runtime system, extends traditional architecture description languages to formally describe itself and naturally inherit plentiful semantics in traditional views of SA, and utilizes reflective middleware to observe and manipulate the runtime system. In order to demonstrate the feasibility of this approach, a reflective J2EE application server, called PKUAS, is implemented to observe and manipulate the components, connectors and constraints in the runtime system. Finally, the performance evaluation proves that making RSA explicit and operable at runtime has little effect on the runtime system.

Knowledge-based repository scheme for storing and retrieving business components: a theoretical design and an empirical analysis

Component-based development (CDB) promises to reduce complexity and cost of software development and maintenance through reuse. For CBD to be successful, a vibrant market for commercial business components is essential. One of the key requirements of an active market for business components is an effective scheme for classifying and describing them at various levels of detail, as well as a corresponding repository for storing and retrieving these components. Such a scheme needs to support various constituents such as business users, managers, and application assemblers. The scheme and repository should help users and managers to select components that match their requirements and aid application assemblers in identifying components most compatible with their deployment environment (such as the platform) and system inputs (such as data types). Drawing from the concepts of group technology and software reuse paradigm, this paper proposes a scheme for classifying and describing business components and the design of a knowledge-based repository for their storage and retrieval. The proposed scheme is implemented in a prototype repository. The effectiveness of the prototype and the underlying classification and coding scheme is assessed empirically through controlled experiments. Results support the assertion that the scheme is effective in enhancing the users' and analysts' ability to find the needed business components.

Software quality improvement in France telecom research center

AbstractThe needs and business goals of France Telecom R&D are centered around achieving enhanced customer satisfaction and greater competitiveness. These key management concerns become drivers that initiate software process improvement with goals of higher software quality, lower development and maintenance costs, shorter time to market, and increased predictability and control of software products and processes.Software process improvement is considered as a continuous process; France Telecom R&D moves continually around a stabilization cycle, Standardize Do Check Act (SDCA) and an improvement cycle, Plan Do Check Act(PDCA). Within these cycles, in a series of steps of specific improvement, actions such as introducing new or improved practices into software processes are accomplished.The software process model is based on the ISO/IEC 12207 standard ‘Software Life‐Cycle Processes’, and the assessment method uses the ISO/IEC 15504 standard ‘Software process Assessment’. Some new processes are defined and used in the area of software engineering. Copyright © 2004 John Wiley & Sons, Ltd.

Measuring the maintainability of a communication protocol based on its formal specification

It is difficult to measure the maintainability of a software system early in the development life cycle from its requirement descriptions written in a natural language because informal specifications cannot be analyzed. With the uses of formal description techniques (FDTs) in the communication protocol area since the mid-1980s, avenues have been opened up for a system to be analyzed early in the specification phase. Quantitative measures on its maintainability can then be extracted from such a formal specification, so that we can develop easily maintainable communication software systems and further reduce the increasingly high cost of software maintenance. To date, there is hardly any work done on measuring the maintainability of a system early in its specification phase. This paper presents a method for measuring the maintainability of a communication by using maintainability metrics derived from its formal specification written in Estelle. The methodology for building the Estelle maintainability metrics hierarchy is presented. We have also developed an automated tool, called PSAMS, to automate the calculation of the maintainability indices. We also found that there is a significant correlation between the specification metrics proposed and the widely adopted implementation metrics, thus demonstrating that our proposed metrics are a reliable means of measuring the maintainability of a communication protocol early in the specification phase.

Predicting maintenance performance using object-oriented design complexity metrics

The Object-Oriented (OO) paradigm has become increasingly popular in recent years. Researchers agree that, although maintenance may turn out to be easier for OO systems, it is unlikely that the maintenance burden will completely disappear. One approach to controlling software maintenance costs is the utilization of software metrics during the development phase, to help identify potential problem areas. Many new metrics have been proposed for OO systems, but only a few of them have been validated. The purpose of this research is to empirically explore the validation of three existing OO design complexity metrics and, specifically, to assess their ability to predict maintenance time. This research reports the results of validating three metrics, Interaction Level (IL), Interface Size (IS), and Operation Argument Complexity (OAC). A controlled experiment was conducted to investigate the effect of design complexity (as measured by the above metrics) on maintenance time. Each of the three metrics by itself was found to be useful in the experiment in predicting maintenance performance.

Integrating Outsourcing in the Maintenance Process

Outsourcing of software life cycle activities is a growing business area in many sectors influenced by Information Technologies. This fact, coupled with the usual lack of planning and high costs of software maintenance, may invite many organizations to outsource this important process of the software life cycle. Such outsourcing should be relied to a “technological associate” who can carry out this process using an adequate methodological foundation. In this paper we present the outsourcing strategy that we have integrated in MANTEMA, a methodology for software maintenance developed by our university and Atos ODS, a multinational organization which provides software maintenance services to third-party organizations.

Using interchangeable virtual instrument (IVI) drivers to increase test system performance

Instrument driver technology has evolved tremendously over the last decade. Initiatives such as SCPI and VXIplug&play have simplified remote instrument programming with standardized command messages and high-level utility functions. The latest initiative is the Interchangeable Virtual Instrument (IVI) Foundation. IVI defines standards for instrument drivers that offer benefits to provide higher performance. These benefits include instrument interchangeability, instrument simulation, and instrument state-caching. Because instrument drivers are an integral part of a modern test system, IVI promises to provide developers with the tools to increase their system performance significantly. This paper reviews the technical aspects of the IVI specification and the TVI architecture and describes how IVI can help the test system developer improve performance and lower long-term software maintenance costs.

Empirical studies of a prediction model for regression test selection

Regression testing is an important activity that can account for a large proportion of the cost of software maintenance. One approach to reducing the cost of regression testing is to employ a selective regression testing technique that: chooses a subset of a test suite that was used to test the software before the modifications; then uses this subset to test the modified software. Selective regression testing techniques reduce the cost of regression testing if the cost of selecting the subset from the test suite together with the cost of running the selected subset of test cases is less than the cost of rerunning the entire test suite. Rosenblum and Weyuker (1997) proposed coverage-based predictors for use in predicting the effectiveness of regression test selection strategies. Using the regression testing cost model of Leung and White (1989; 1990), Rosenblum and Weyuker demonstrated the applicability of these predictors by performing a case study involving 31 versions of the KornShell. To further investigate the applicability of the Rosenblum-Weyuker (RW) predictor, additional empirical studies have been performed. The RW predictor was applied to a number of subjects, using two different selective regression testing tools, Deja vu and TestTube. These studies support two conclusions. First, they show that there is some variability in the success with which the predictors work and second, they suggest that these results can be improved by incorporating information about the distribution of modifications. It is shown how the RW prediction model can be improved to provide such an accounting.

A model for addressing software volatility in new system development

Software maintenance costs are well-documented as the largest component of software life cycle cost. While significant research has been conducted on the characterization of maintainability, program understanding, and software organization for maintainability, there is little work published on the a priori identification and encapsulation of a software system’s volatile points. In order to validate the fundamental behavior of enhancive software volatility, a volatility-oriented maintenance life cycle cost model is presented to describe the relationship between frequency, cost-to-change, encapsulation strategies, and the resulting life cycle cost. The behavior of this model is then correlated against historical change data from systems of a single domain. The paper also outlines a methodology for identifying the most probable points in software to experience the highest frequency of change, and using this postulated information as guidance in developing tools, organization, and techniques to make these points easy to change.

Reducing maintenance costs through the application of modern software architecture principles

Large software programs are usually long lived and continually evolve. Substantial maintenance effort is often extended by engineers trying to understand the software prior to making changes. To successfully evolve the software, a thorough understanding of the architect's intentions about software organization is required. Software maintenance costs can be reduced significantly if the software architecture is well defined, clearly documented, and creates an environment that promotes design consistency through the use of guidelines and design patterns. Building a maintainable system depends upon the consistent application of these architectural practices. This paper describes the application of modern software architecture methods to achieve a maintainable implementation of a large, distributed, real-time, embedded software system.

Tool support for planning the restructuring of data abstractions in large systems

Restructuring software to improve its design can lower software maintenance costs. One problem encountered during restructuring is formulating the new design. A meaning-preserving program restructuring tool with a star diagram manipulable visualization can help a programmer redesign a program based on abstract data types. However, the transformational support required for meaning-preserving restructuring is costly to provide. Also, programmers encounter comprehension and recall difficulties in complex restructuring tasks. Consequently, transformations were replaced with visual and organizational aids that help a programmer to plan and carry out a complex restructuring. For example, a star diagram manipulation called trimming was added, which mimics the way that basic restructuring transformations affect the star diagram display, allowing a programmer to plan a restructuring without depending upon restructuring transformations. With the ability to annotate trimmed star diagram components, plans can be recorded and later recalled. Programmer-controlled elision was added to help remove clutter from star diagram views. We implemented a star diagram planning tool for C programs, measured its elision capabilities, and performed a programmer study. We found that elision is effective in controlling star diagram size, and the study revealed that each programming team successfully planned its restructuring in rather different, unanticipated ways. These experiments resulted in important improvements in the tool's software design and user interface.