How reuse influences productivity in object-oriented systems

Basili, Victor R.; Briand, Lionel C.; Melo, Walcélio L.

doi:10.1145/236156.236184

Cited by 203 publications

(119 citation statements)

References 11 publications

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“…In addition to the sparseness of the results, the question of practical significance is rarely discussed. We also searched for experiments in artificial settings, which only added one student experiment to the search results (Basili et al, 1996) that is not included in this review.…”

Section: Review Framework and Research Questionsmentioning

confidence: 99%

Quality, productivity and economic benefits of software reuse: a review of industrial studies

2007

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Abstract. Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by votecounting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry.

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Section: Review Framework and Research Questionsmentioning

confidence: 99%

Quality, productivity and economic benefits of software reuse: a review of industrial studies

2007

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“…Many determinants of the studied projects were the same as the determinants of pre-agile projects [2,3,12,17]. Requirements could only be stabilized when the product and product use were clear enough.…”

Section: Discussionmentioning

confidence: 96%

“…The ability to plan is a key determinant: requirements engineering, prototyping, and reuse reduce the need for error correction and rework [2,3]. Requirements engineering, in particular, enables effort estimation, project negotiation, progress tracking, and high test coverage [12].…”

Section: Related Workmentioning

confidence: 99%

Perspectives on Productivity and Delays in Large-Scale Agile Projects

Badampudi

Fricker

Moreno

2013

Lecture Notes in Business Information Processing

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Abstract. Many large and distributed companies run agile projects in development environments that are inconsistent with the original agile ideas. Problems that result from these inconsistencies can affect the productivity of development projects and the timeliness of releases. To be effective in such contexts, the agile ideas need to be adapted. We take an inductive approach for reaching this aim by basing the design of the development process on observations of how context, practices, challenges, and impacts interact. This paper reports the results of an interview study of five agile development projects in an environment that was unfavorable for agile principles. Grounded theory was used to identify the challenges of these projects and how these challenges affected productivity and delays according to the involved project roles. Productivity and delay-influencing factors were discovered that related to requirements creation and use, collaboration, knowledge management, and the application domain. The practitioners' explanations about the factors' impacts are, on one hand, a rich empirical source for avoiding and mitigating productivity and delay problems and, on the other hand, a good starting point for further research on flexible large-scale development.

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“…Deepti et al [15] measure the complexity with the help of CCI (Class Complexity due to Inheritance) and ACI (Average Complexity of a program due to inheritance) metrics. But Harrison, Counsell and Nithi [12] contradict through experimental assessment that systems without inheritance are easier to understand and modify. The main reason is that to inherit a new class, it is necessary to understand the implementation of parent as well as any of the parent's ancestors.…”

Section: Related Workmentioning

confidence: 99%