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Programming languages ranking based on energy measurements

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Abstract

Software is developed using programming languages whose choice is made based on a wide range of criteria, but it should be noted that the programming language selected can affect the quality of the software product. In this paper, we focus on analysing the differences in energy consumption when running certain algorithms that have been developed using different programming languages. Therefore, we focus on the software quality from the perspective of greenability, in concrete in the aspects related to energy efficiency. For this purpose, this study has conducted an empirical investigation about the most suitable programming languages from an energy efficiency perspective using a hardware-based consumption measurement instrument that obtains real data about energy consumption. The study builds upon a previous study in which energy efficiency of PL were ranked using a software-based approach where the energy consumption is an estimation. As a result, no significant differences are obtained between two approaches, in terms of ranking the PL. However, if it is required to have a more realistic knowledge of consumption, it is necessary to use hardware approaches. Furthermore, the hardware approach provides information about the energy consumption of specific DUT hardware components, such as, HDD, graphics card, and processor, and a ranking for each of component is elaborated. This can provide useful information to make a more informed decision on the choice of a PL, depending on several factors, such as the type of algorithms to be implemented, or the effects on power consumption not only in overall, but also depending on specific DUT hardware components.

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Acknowledgements

The work has received support from the following projects: OASSIS (PID2021-122554OB-C31/ AEI/10.13039/ 501100011033/FEDER, UE); EMMA (Project SBPLY/21/180501/000115, funded by CECD (JCCM) and FEDER funds); SEEAT (PDC2022-133249-C31 funded by MCIN/AEI/ https://doi.org/10.13039/501100011033 and European Union NextGenerationEU/PRTR); PLAGEMIS (TED2021-129245B-C22 funded by MCIN/AEI/ https://doi.org/10.13039/501100011033 and European Union NextGenerationEU/PRTR). Financial support for the execution of applied research projects, within the framework of the UCLM Own Research Plan, co-financed at 85% by the European Regional Development Fund (FEDER) UNION (2022-GRIN-34110).

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Authors and Affiliations

  1. Institute of Technology and Information Systems, University of Castilla-La Mancha, Ciudad Real, Spain

    Alberto Gordillo, Coral Calero, Mª Ángeles Moraga & Félix García

  2. New York University, Abu Dhabi, UAE

    João Paulo Fernandes

  3. DEI-FEUP, University of Porto, Porto, Portugal

    Rui Abreu

  4. University of Minho, Minho, Portugal

    João Saraiva

Authors
  1. Alberto Gordillo
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  2. Coral Calero
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  4. Félix García
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  5. João Paulo Fernandes
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  6. Rui Abreu
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  7. João Saraiva
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Contributions

The planning of the comparison was carried out by Alberto Gordillo, Coral Calero, Mª Moraga, Félix García, João Paulo Fernandes, Rui Abreu, and João Saraiva. The preparation of the replication was conducted by Alberto Gordillo, Coral Calero, Mª Moraga, and Félix García. The experiment execution was performed by Alberto Gordillo. The manuscript was written by Alberto Gordillo, Coral Calero, Mª Moraga, and Félix García. Finally, the comparison of the studies and the review of both the experiment and the manuscript were conducted by Alberto Gordillo, Coral Calero, Mª Moraga, Félix García, João Paulo Fernandes, Rui Abreu, and João Saraiva.

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Correspondence to Alberto Gordillo.

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Appendices

Appendix A

Table 13 Mean values of energy and time of the 14 program languages in all algorithms. Table 13 shows the time and energy result of all algorithms for each programming language
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Table 14 Standard deviation of energy of the 14 program languages in all algorithms Table 14
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Appendix B

Figure 10 shows the clusters of the programming languages for each algorithm, using Ward's method

Fig. 10
figure 10

Clusters of Programming languages for each algorithm considering energy consumption

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Appendix C

Table 15 Mean values of energy for components of the 14 program languages in all algorithms. Table 15 provides an overview of the energy consumption for individual components associated with each programming language for the different algorithms
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Table 16 Standard deviation of the measurements for each component of the 14 programming languages for all. Algorithms. Table 16 shows the standard deviations between measurements, sorted by algorithm and language
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Figures 11, 12 and 13 show the clusters identified based on the energy consumed by the hard disk, graphics card and processor respectively. Figure 11 HDD clusters by algorithm Fig. 12 Graphics Card clusters by algorithm.

Fig. 11
figure 11

HDD clusters by algorithm

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Fig. 12
figure 12

Graphics Card clusters by algorithm

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Fig. 13
figure 13

Processor clusters by algorithm

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Gordillo, A., Calero, C., Moraga, M.Á. et al. Programming languages ranking based on energy measurements. Software Qual J 32, 1539–1580 (2024). https://doi.org/10.1007/s11219-024-09690-4

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