Effort Estimation Factors for Corrective Software Maintenance Projects: A Qualitative Analysis of Estimation Criteria

In this paper, we identify factors that impact software maintenance effort by exploring expert software maintenance estimators’ knowledge about corrective maintenance projects. We use a qualitative approach to identify the issues important to these experts to derive their effort estimates. We find seventeen factors (rated and rank ordered by importance) that affect corrective maintenance effort and include constructs related to developers, code, defects, and environment. Several of these factors that have a comparably strong influence on corrective maintenance estimation are unique to corrective maintenance and are not generally observed in established software estimation models. The results enhance organizations’ ability to effectively manage maintenance environments by focusing attention on the identified areas. For future research, these results represent an important step toward developing a comprehensive and accurate corrective maintenance effort estimation model

Analytical model of information system development productivity in adaptive and perfective maintenance phases

Copyright © 2018 Inderscience Enterprises Ltd. This paper presents an analytical model for information system (IS) maintenance productivity in adaptive and perfective phases. The modelling approach is from economic view point. The productivity model considers the economic value of the maintenance phase, pre-committed fixed cost and variable cost consumed in adaptive/perfective maintenance. Influence factors to the productivity are analysed using simulation. The simulation provides a tool for IS project managers to tune the project parameters to obtain the optimal productivity in adaptive/perfective maintenance phases

Primary Drivers of Software Maintenance Cost Studied Using Longitudinal Data

We examine the main drivers of software maintenance effort and cost. We use the ‘Distributed Cognition’ framework to hypothesize about how ‘discovery work’ in maintenance is effected by two types of cost drivers: system attributes (size, complexity, age, etc.) and personnel attributes (number of maintainers, location dispersion, etc.). We test our hypotheses using archival data about over 5,000 maintenance projects carried out between 2009 and 2011 on 412 different operational systems in a large financial institution. We find that personnel attributes are significantly more influential than system attributes. In particular, a marginal change in personnel factors is associated with effort growing much faster than cost, indicating an escalating marginal cost of spreading maintenance work across more maintainers and site locations. We also find, counter to expectation, that two system attributes are negatively linked to maintenance effort and cost. Implications of these findings for research and practices are discussed

Model-based risk assessment

In this research effort, we focus on model-based risk assessment. Risk assessment is essential in any plan intended to manage software development or maintenance process. Subjective techniques are human intensive and error-prone. Risk assessment should be based on architectural attributes that we can quantitatively measure using architectural level metrics. Software architectures are emerging as an important concept in the study and practice of software engineering nowadays, due to their emphasis on large-scale composition of software product, and to their support for emerging software engineering paradigms, such as product line engineering, component based software engineering, and software evolution.;In this dissertation, we generalize our earlier work on reliability-based risk assessment. We introduce error propagation probability in the assessment methodology to account for the dependency among the system components. Also, we generalize the reliability-based risk assessment to account for inherent functional dependencies.;Furthermore, we develop a generic framework for maintainability-based risk assessment which can accommodate different types of software maintenance. First, we introduce and define maintainability-based risk assessment for software architecture. Within our assessment framework, we investigate the maintainability-based risk for the components of the system, and the effect of performing the maintenance tasks on these components. We propose a methodology for estimating the maintainability-based risk when considering different types of maintenance. As a proof of concept, we apply the proposed methodology on several case studies. Moreover, we automate the estimation of the maintainability-based risk assessment methodology

Testing and Maintenance in the Video Game Industry Today

Testing and maintenance are important when designing any type of software, especially video games. Since the gaming industry began, testing and maintenance techniques have evolved and changed. In order to understand how testing and maintenance techniques are practiced in the gaming industry, several key elements must be examined. First, specific testing and maintenance techniques that are most useful for video games must be analyzed to understand their effectiveness. Second, the processes used for testing and maintaining video games at the beginning of the industry must be reviewed in order to see how far testing and maintenance techniques have progressed. Third, the potential negative side effects of new testing and maintenance techniques need to be evaluated to serve as both a warning for future game developers and a way of improving the overall quality of current video games

Identifying Effort Estimation Factors for Corrective Maintenance in Object-Oriented Systems

This research explores the decision-making process of expert estimators of corrective maintenance projects by usingqualitative methods to identify the factors that they use in deriving estimates. We implement a technique called causalmapping, which allows us to identify the cognitive links between the information that estimators use, and the estimates thatthey produce based on that information. Results suggest that a total of 17 factors may be relevant for corrective maintenanceeffort estimation, covering constructs related to developers, code, defects, and environment. This line of research aims ataddressing the limitations of existing maintenance estimation models that do not incorporate a number of soft factors, thus,achieving less accurate estimates than human experts

A Comparative study of Traditional and Component based software engineering approach using models

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Proceedings of the 19th Annual Software Engineering Workshop

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of applications software. The goals of the SEL are: (1) to understand the software development process in the GSFC environment; (2) to measure the effects of various methodologies, tools, and models on this process; and (3) to identify and then to apply successful development practices. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that include this document