Assessment of Geant4 Maintainability with respect to software engineering references

Ronchieri, Elisabetta; Pia, Maria Grazia; Canaparo, Marco

doi:10.1088/1742-6596/898/7/072042

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Our approach (summarized in Figure 1) uses as input a subset of the Geant4 software dataset composed of 482 modules with 66 software metrics and 34 releases. This dataset has been obtained by applying a tool for the static analysis -Imagix 4D tool [19] -to the various modules of several software releases [20]. Imagix 4D tool's output has been preprocessed in order to keep only the common software modules and software metrics among the Geant4 software versions.…”

Section: Methodsmentioning

confidence: 99%

Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques

et al. 2020

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Software defect prediction is an activity that aims at narrowing down the most likely defect-prone software modules and helping developers and testers to prioritize inspection and testing. This activity can be addressed by using Machine Learning techniques applied to software metrics datasets that are usually unlabelled, i.e. they lack modules classification in terms of defectiveness. To overcome this limitation, in addition to the usual data pre-processing operations to manage mission values and/or to remove inconsistencies, researches have to adopt an approach to label their unlabelled software datasets. The extraction of defectiveness data to label all the instances of the datasets is an extremely time and effort consuming operation. In literature, many studies have introduced approaches to build a defect prediction models on unlabelled datasets. In this paper, we describe the analysis of new unlabelled datasets from WLCG software, coming from HEP-related experiments and middleware, by using Machine Learning techniques. We have experimented new approaches to label the various modules due to the heterogeneity of software metrics distribution. We discuss a number of lessons learned from conducting these activities, what has worked, what has not and how our research can be improved.

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Section: Methodsmentioning

confidence: 99%

Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques

et al. 2020

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“…The unlabelled datasets derive from the Geant4 [8] software -a toolkit for the simulation of the passage of particles through matter. For this software, effort has been dedicated to collect software metrics for the different modules and 34 software releases [16], and to analyse them with statistical methods [17].…”

Section: Input Setsmentioning

confidence: 99%