Modeling Variabilities from Software Process Lines with Compositional and Annotative Techniques: A Quantitative Study

Aleixo, Fellipe Araújo; Kulesza, Uirá; OliveiraJr, Edson

doi:10.1007/978-3-642-39259-7_14

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Aleixo, Kulesza, and Oliveira Junior [2] presented a quantitative comparative study of software process lines, which are a set of software processes with a common base. The comparative study was used to evaluate two approaches to modeling software process lines: the compositional and the annotative approaches.…”

Section: Managing Software Processesmentioning

confidence: 99%

Recent developments in product-focused software process improvement

Graziotin

Jedlitschka

2013

SIGSOFT Softw. Eng. Notes

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This report summarizes the presentations and discussions that happened at PROFES 2013, the 14th International Conference on ProductFocused Software Process Improvement, which was held June 12-14, 2013 in Paphos, Cyprus. The main theme of PROFES is software process improvement (SPI) motivated by product, process, and service quality needs. PROFES 2013 addressed both quality engineering and management topics, divided into the areas of Decision Support in

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Section: Managing Software Processesmentioning

confidence: 99%

Recent developments in product-focused software process improvement

Graziotin

Jedlitschka

2013

SIGSOFT Softw. Eng. Notes

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“…Aleixo et al [11] inspired us to investigating the compositional approach represented by EPF with relation to SMartySPEM. Then, Dias and OliveiraJr [9] and Dias et al [10] compared SMartySPEM with EPF providing preliminary empirical evidence of SMartySPEM effectiveness over EPF as we can see in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Such an approach allows the automatic derivation of software process instances from an SPrL. Then, in 2013, Aleixo et al [11] compared compositional (eclipse process framework – EPF) with annotative (GenArch‐P) approaches for modelling SPrL. Such study is important as the compositional approach provided, in this case, slight better results than GenArch‐P for different evaluation criteria, such as modularity.…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…We have previously investigated compositional versus annotative approaches for software process variability modelling [9][10][11]. In an overall analysis, annotative approach has taken advantage over compositional as gathered initial evidence from Dias and OliveiraJr [9], Dias et al [10], and Aleixo et al [11]. In addition, our Research Group on Systematic Software Reuse and Experimentation (GRSSE) has proposed an annotative approach for modelling variability in software process, named stereotypebased variability management for the SPEM meta-model (SMartySPEM) [12].…”

Section: Introductionmentioning

confidence: 99%

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Empirical study on software process variability modelling with SMartySPEM and vSPEM

2018

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With the continuous improvement of software processes, it is possible to increase quality, to address different application domains and accelerate the development of software products. However, existing process modelling notations like the SPEM meta-model typically do not have appropriate constructs for expressing process variability. Thus, SPEM-based approaches such as SMartySPEM and vSPEM provide mechanisms for representing variabilities to address characteristics of different projects. Here, the authors empirically compared SMartySPEM and for variability representation in software processes models, aiming to analyze correction, time, and efficiency. The performed experiment provides preliminary evidence based on 11 participants and sample size N = 44. Participants took longer to comprehend diagrams in SMartySPEM. However, the correctness of SMartySPEM diagrams had a superior result and higher efficiency based on the median values and hypothesis tests. With regard to variability mechanisms, the diagrams modelled with SMartySPEM had a slightly lower efficiency and it took longer for modifying them, thus the correctness of the SMartySPEM diagrams had a superior result. Therefore, an initial body of knowledge indicated a positive efficiency of SMartySPEM for variability representation in process models, as well as potential improvements as the reduction in SMartySPEM diagrams complexity, which may contribute to its evolution.

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