On understanding laws, evolution, and conservation in the large-program life cycle

Lehman, M. M.

doi:10.1016/0164-1212(79)90022-0

Cited by 286 publications

(185 citation statements)

References 3 publications

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“…Four perspectives have been considered: (1) changes that occurred in the production / test code, (2) branch coverage obtained during the lifespan of the project, (3) number of versions that did not compile because of test failures, and (4) ratio between the amount of test code and production code. An overview of this preliminary analysis is shown in Table II.…”

Section: B Preliminary Analysismentioning

confidence: 99%

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Studying Fine-Grained Co-evolution Patterns of Production and Test Code

Marșavina

Romano

Zaidman

2014

2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation

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Abstract-Numerous software development practices suggest updating the test code whenever the production code is changed. However, previous studies have shown that co-evolving test and production code is generally a difficult task that needs to be thoroughly investigated.In this paper we perform a study that, following a mixed methods approach, investigates fine-grained co-evolution patterns of production and test code. First, we mine fine-grained changes from the evolution of 5 open-source systems. Then, we use an association rule mining algorithm to generate the co-evolution patterns. Finally, we interpret the obtained patterns by performing a qualitative analysis.The results show 6 co-evolution patterns and provide insights into their appearance along the history of the analyzed software systems. Besides providing a better understanding of how test code evolves, these findings also help identify gaps in the test code thereby assisting both researchers and developers.

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Section: B Preliminary Analysismentioning

confidence: 99%

“…Lehman has taught us that a software system must evolve, or it becomes progressively less useful [1]. During this evolution, the system's source code continuously changes to cope with new requirements or possible issues that might arise.…”

Section: Introductionmentioning

confidence: 99%

Studying Fine-Grained Co-evolution Patterns of Production and Test Code

Marșavina

Romano

Zaidman

2014

2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation

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“…Such changes can lead to potential side effects and/or violations of the underlying assumptions. Software-change prediction methodologies that provide all the entities that need to be appropriately co-changed are important for sustained evolution of a software system [5,14]. Two broad groups of methodologies are described in the literature for supporting software changes.…”

Section: Introductionmentioning

confidence: 99%

Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction

Kagdi

Maletic

2007

Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)

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“…Lee et al [9] provide an overview of open source software evolution with software metrics. The authors explored the evolution of an open source software system in terms of size, coupling and cohesion, and discuss its quality change based on the Lehman's laws of evolution [4], [5], [27]. Jermakovics et al [28] propose an approach to visually identify software evolution patterns related to requirements.…”

Section: Related Workmentioning

confidence: 99%

Metrics and Software Quality Evolution: A Case Study on Open Source Software

Drouin¹,

Badri²,

Touré³

2013

IJCTE

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Abstract-This paper aims at analyzing empirically the quality evolution of an open source software using metrics. We used a control flow based metric (Quality Assurance IndicatorQi) which we proposed in a previous work. We wanted to investigate if the Qi metric can be used to observe how quality evolves along the evolution of the successive released versions of the subject software system. We addressed software quality from an internal perspective. We performed an empirical analysis using historical data on the subject system (Apache Tomcat). The collected data cover, in fact, a period of more than seven years (thirty-one versions in total). Empirical results provide evidence that the Qi metric reflects properly the quality evolution of the subject system.

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On understanding laws, evolution, and conservation in the large-program life cycle

Cited by 286 publications

References 3 publications

Studying Fine-Grained Co-evolution Patterns of Production and Test Code

Studying Fine-Grained Co-evolution Patterns of Production and Test Code

Combining Single-Version and Evolutionary Dependencies for Software-Change Prediction

Metrics and Software Quality Evolution: A Case Study on Open Source Software

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