Mega Software Engineering

Inoue, Katsuro; Garg, Pankaj; Iida, Hajimu; Matsumoto, Kenichi; Torii, Koji

doi:10.1007/11497455_32

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…In terms of large size code clone analysis (also referred to as Mega Software Engineering [11]) a distributed CCFinder has been implemented by Liverie et al [12]. They have analyzed 400 million lines of code with a cluster of 80 machines.…”

Section: Clone Detectionmentioning

confidence: 99%

“…Code clones do play an influential role in lowering costs associated with these tasks [1,2]. While previous research has mainly focused on code clones on a per-project basis, the availability of semantic-web standards and the following wide-spread publication of code and its semantics as linked-data has opened up new possibilities to analyze code on a global level [11]. Global code clones can help identify already existing similar code in other published projects during development or bring a related piece of code to the attention of a maintainer while fixing a bug.…”

Section: Introductionmentioning

confidence: 99%

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Scalable clone detection using description logic

Schügerl

2011

Proceedings of the 5th International Workshop on Software Clones

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The semantic web is slowly transforming the web as we know it into a machine understandable pool of information that can be consumed and reasoned about by various clients. Source-code is no exception to this trend and various communities have proposed standards to share code as linked data. With the availability of large amounts of open source code published in publically accessible repositories and the introduction of massively horizontally scaling frameworks and cloud computing infrastructure, a new era of software mining across information silos is reshaping the software engineering landscape. The so far unreachable goal of analyzing code at a global level, and therefore detecting global software clones, has become manageable. Description logic and semantic web reasoners have so far only plaid a minor role in this transformation and are mainly used to model source code data. In this paper, we introduce a clone detection algorithm that uses a semantic web reasoner and is based on the Hadoop map-reduce framework that can scale horizontally to a large amount of data. We also define a novel and compact clone model that only considers control-blocks and used data types while still yielding similar clone detection results than more complex representations. In order to validate our approach we have compared our algorithm to some of the leading clone detection tools (CCFinder, JCD and Simian) and show differences in performance and detection precision.

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Section: Clone Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scalable clone detection using description logic

Schügerl

2011

Proceedings of the 5th International Workshop on Software Clones

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“…The MG2C server was used to create the chromosomal location map (http://mg2c.iask.in/mg2c_v2.1/; accessed on 23 July 2023) [92]. Protein multiple-amino-acid sequence alignment (MSA) was performed with the cluster W algorithm using MEGA 11 [93]. The phylogenetic tree was generated with the use of the maximum likelihood method with 1000 bootstraps and visualized with the iTOL v6.8 web tool (https://itol.embl.de/upload.cgi; accessed on 22 July 2023) [94].…”

Section: Chromosome Location and Phylogenetic Analysis Of The Tricace...mentioning

confidence: 99%

Genome-Wide Identification and Expression Analysis of Catalase Gene Families in Triticeae

Ghorbel,

Zribi,

Haddaji

et al. 2023

Plants

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Aerobic metabolism in plants results in the production of hydrogen peroxide (H2O2), a significant and comparatively stable non-radical reactive oxygen species (ROS). H2O2 is a signaling molecule that regulates particular physiological and biological processes (the cell cycle, photosynthesis, plant growth and development, and plant responses to environmental challenges) at low concentrations. Plants may experience oxidative stress and ultimately die from cell death if excess H2O2 builds up. Triticum dicoccoides, Triticum urartu, and Triticum spelta are different ancient wheat species that present different interesting characteristics, and their importance is becoming more and more clear. In fact, due to their interesting nutritive health, flavor, and nutritional values, as well as their resistance to different parasites, the cultivation of these species is increasingly important. Thus, it is important to understand the mechanisms of plant tolerance to different biotic and abiotic stresses by studying different stress-induced gene families such as catalases (CAT), which are important H2O2-metabolizing enzymes found in plants. Here, we identified seven CAT-encoding genes (TdCATs) in Triticum dicoccoides, four genes in Triticum urartu (TuCATs), and eight genes in Triticum spelta (TsCATs). The accuracy of the newly identified wheat CAT gene members in different wheat genomes is confirmed by the gene structures, phylogenetic relationships, protein domains, and subcellular location analyses discussed in this article. In fact, our analysis showed that the identified genes harbor the following two conserved domains: a catalase domain (pfam00199) and a catalase-related domain (pfam06628). Phylogenetic analyses showed that the identified wheat CAT proteins were present in an analogous form in durum wheat and bread wheat. Moreover, the identified CAT proteins were located essentially in the peroxisome, as revealed by in silico analyses. Interestingly, analyses of CAT promoters in those species revealed the presence of different cis elements related to plant development, maturation, and plant responses to different environmental stresses. According to RT-qPCR, Triticum CAT genes showed distinctive expression designs in the studied organs and in response to different treatments (salt, heat, cold, mannitol, and ABA). This study completed a thorough analysis of the CAT genes in Triticeae, which advances our knowledge of CAT genes and establishes a framework for further functional analyses of the wheat gene family.

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“…The concept of very-large size code clone analysis relates to the notion of Mega Software Engineering [10]. Mega Software Engineering refers to a collection of software engineering technologies that, supporting the large scale analysis of data from many projects, enables process improvement at organization level, rather than at single project level.…”

Section: Related Workmentioning

confidence: 99%

Very-Large Scale Code Clone Analysis and Visualization of Open Source Programs Using Distributed CCFinder: D-CCFinder

Livieri

Higo

Matushita

et al. 2007

29th International Conference on Software Engineering (ICSE'07)

Self Cite

116

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The increasing performance-price ratio of computer hardware makes possible to explore a distributed approach at code clone analysis. This paper presents D-CCFinder, a distributed approach at large-scale code clone analysis. D-CCFinder has been implemented with 80 PC workstations in our student laboratory, and a vast collection of open source software with about 400 million lines in total has been analyzed with it in about 2 days. The result has been visualized as a scatter plot, which showed the presence of frequently used code as easy recognizable patterns. Also, D-CCFinder has been used to analyze a single software system against the whole collection in order to explore the presence of code imported from open source software.

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Mega Software Engineering

Cited by 6 publications

References 19 publications

Scalable clone detection using description logic

Scalable clone detection using description logic

Genome-Wide Identification and Expression Analysis of Catalase Gene Families in Triticeae

Very-Large Scale Code Clone Analysis and Visualization of Open Source Programs Using Distributed CCFinder: D-CCFinder

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