Q-MOPP: qualitative evaluation of maintenance organizations, processes and products

Briand, Lionel C.; Kim, Yong‐Mi; Melo, Walcélio L.; Seaman, Carolyn; Basili, Victor R.

doi:10.1002/(sici)1096-908x(199807/08)10:4<249::aid-smr172>3.0.co;2-7

Cited by 16 publications

(9 citation statements)

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“…Published evaluations of software maintenance practices tend to concentrate on the narrow issue of efficiently handling change requests and bug fixes [11][12][13][14]. For example, a common denominator in these papers is the emphasis that is placed on the presence of what is termed a bug tracking system, historical database of changes, or change management.…”

Section: Related Workmentioning

confidence: 99%

Software maintenance from a service perspective

Niessink

Vliet

2000

J. Softw. Maint: Res. Pract.

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The lanthanum cerium in the fiber is formed by calcination of the electrospinning fibers of polymer/inorganic composite at 600 °C and a polycrystalline hollow fiber with diameter of 1∼5 μm can be obtained at 1000 °C for 12 h. After calcination at 1400 °C, the lanthanum cerium grains are closely connected to each other with clear grain boundaries and the hollow structure can be observed in the cross‐section of the fiber.

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Section: Related Workmentioning

confidence: 99%

Software maintenance from a service perspective

Niessink

Vliet

2000

J. Softw. Maint: Res. Pract.

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“…Another example of using qualitative methods in studying maintenance was Briand et al's study of a NASA maintenance organization [2].…”

Section: Methodsmentioning

confidence: 99%

The information gathering strategies of software maintainers

Seaman

International Conference on Software Maintenance, 2002. Proceedings.

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In examining software maintenance processes for improvement opportunities, an obvious choice is information jlo}\-'. Obtaining accurate, up-to-date, and useful information about a system being maintained is a major task. It is also a difficult task because the sources of this information are often limited, inaccessible, or unkno}\-.n. Clearly this impacts maintenance productivity -simply because of the time it takes to find and use the appropriate information sources -as }\-'ell as the quality of system changes, which depends on the quality of the system information available.This paper describes the results of a survey study that aims to discover the information gathering strategies that soft}\-'are maintainers employ. The survey }\-.as completed by 45 software professionals in t}\-'O different organizations with varying degrees of experience in maintenance.Their responses. on the sulface, simply show that maintainers overwhelmingly reryon source code. which is not surprising. Ho }\-'ever, a deeper analysis of the responses show that other sources of information; in particular human sources. some types of CASE support, and lessons learned recorded from previous projects are at least as valuable than source code under some conditions. The results of this ongoing survey study are meant to determine a set of hypotheses about information gathering strategies. }\-'hich will then be empirically evaluated in future studies.

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“…Given a new project x and its effort value y unknown, the conventional ABE produces the point estimate of y as the weighted sum of the effort values of x's k nearest neighbours: (1) where N k (x) is the neighborhood set of x which comprises k nearest historical projects of x, and w i (x i , x) is the weight function dependent on the features of historical project x i and those of project x. Usually, the summation of weights is set to be 1 for the purpose of normalization.…”

Section: A Mathematical Model Of Abementioning

confidence: 99%

“…Software maintenance is amongst the most complicated activities in the software life cycle [1]. It involves understanding the system, making the revisions, analyzing the impact of modifications, and promoting reuse.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inference Approach for Probabilistic Analogy Based Software Maintenance Effort Estimation

Xie

Goh

2008

2008 14th IEEE Pacific Rim International Symposium on Dependable Computing

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Software maintenance effort estimation is essential for the success of software maintenance process. In the past decades, many methods have been proposed for maintenance effort estimation. However, most existing estimation methods only produce point predictions. Due to the inherent uncertainties and complexities in the maintenance process, the accurate point estimates are often obtained with great difficulties. Therefore some prior studies have been focusing on probabilistic predictions. Analogy Based Estimation (ABE) is one popular point estimation technique. This method is widely accepted due to its conceptual simplicity and empirical competitiveness. However, there is still a lack of probabilistic framework for ABE model. In this study, we first propose a probabilistic framework of ABE (PABE). The predictive PABE is obtained by integrating over its parameter k number of nearest neighbors via Bayesian inference. In addition, PABE is validated on four maintenance datasets with comparisons against other established effort estimation techniques. The promising results show that PABE could largely improve the point estimations of ABE and achieve quality probabilistic predictions.

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Q-MOPP: qualitative evaluation of maintenance organizations, processes and products

Cited by 16 publications

References 0 publications

Software maintenance from a service perspective

Software maintenance from a service perspective

The information gathering strategies of software maintainers

Bayesian Inference Approach for Probabilistic Analogy Based Software Maintenance Effort Estimation

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