Journal of the American Society for Information Science
Database models and managerial institution: 50% model + 50% manager
Management Science
Software maintenance management: changes in the last decade
Journal of Software Maintenance: Research and Practice
Journal of Software Maintenance: Research and Practice
Software sizing and estimating: Mk II FPA (Function Point Analysis)
Software sizing and estimating: Mk II FPA (Function Point Analysis)
The quality of questionnaire based software maintenance studies
ACM SIGSOFT Software Engineering Notes
An empirical study of software maintenance tasks
Journal of Software Maintenance: Research and Practice
Comprehension processes during large scale maintenance
ICSE '94 Proceedings of the 16th international conference on Software engineering
Using risk analysis to manage software maintenance
Journal of Software Maintenance: Research and Practice
Estimating software costs
An experimental study of individual subjective effort estimation and combinations of the estimates
Proceedings of the 20th international conference on Software engineering
Training for software maintenance
Journal of Software Maintenance: Research and Practice
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression Models
IEEE Transactions on Software Engineering
Information systems and organizational change
Communications of the ACM
Software Engineering Economics
Software Engineering Economics
Software Maintenance Management
Software Maintenance Management
PROFES '00 Proceedings of the Second International Conference on Product Focused Software Process Improvement
Project Experience Database: A Report Based on First Practical Experience
PROFES '00 Proceedings of the Second International Conference on Product Focused Software Process Improvement
Journal of Management Information Systems
The Impact of Project Planning Team Experience on Software Project Cost Estimates
Empirical Software Engineering
Software maintenance seen as a knowledge management issue
Information and Software Technology
Difficulties experienced by students in maintaining object-oriented systems: an empirical study
ACE '07 Proceedings of the ninth Australasian conference on Computing education - Volume 66
Characteristics of software engineers with optimistic predictions
Journal of Systems and Software
Estimating software maintenance effort: a neural network approach
ISEC '08 Proceedings of the 1st India software engineering conference
The effect of task order on the maintainability of object-oriented software
Information and Software Technology
How large are software cost overruns? A review of the 1994 CHAOS report
Information and Software Technology
A review of studies on expert estimation of software development effort
Journal of Systems and Software
Functional Link Artificial Neural Networks for Software Cost Estimation
International Journal of Applied Evolutionary Computation
ACM Transactions on Software Engineering and Methodology (TOSEM)
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This study reports results from an empirical study of 54 software maintainers in the software maintenance department of a Norwegian company. The study addresses the relationship between amount of experience and maintenance skills. The findings were, amongst others, as follows. (1) While there may have been a reduction in the frequency of major unexpected problems from tasks solved by very inexperienced to medium experienced maintainers, additional years of general software maintenance experience did not lead to further reduction. More application specific experience, however, further reduced the frequency of major unexpected problems. (2) The most experienced maintainers did not predict maintenance problems better than maintainers with little or medium experience. (3) A simple one-variable model outperformed the maintainers' predictions of maintenance problems, i.e. the average prediction performance of the maintainers seems poor. An important reason for the weak correlation between length of experience and ability to predict maintenance problems may be the lack of meaningful feedback on the predictions.