DOI:
https://doi.org/10.14483/22487638.24865Published:
2026-06-01Issue:
Vol. 30 No. 88 (2026): Abril - JunioSection:
ReviewAnálisis de prácticas para reducir el consumo de energía en las pruebas de software: Una Revisión Sistemática de la Literatura
Analysis of practices to reduce energy consumption in software testing: A Systematic Literature Review
Keywords:
Energía verde, Eficiencia energética, Consumo de energía, Pruebas de software, Ingeniería de Software, Pruebas automatizadas (es).Keywords:
Green energy, Energy efficiency, Energy consumption, Software testing, Software engineering, Automated testing (en).Downloads
Abstract (es)
Contexto: El consumo de energía es importante para el desarrollo tecnológico actual, no solo por su impacto económico sino también por sus implicaciones ambientales. En el desarrollo de software, el consumo se intensifica en etapas con mayor demanda de energía, siendo la prueba una de las más notables. Aunque se han establecido nuevas directrices centradas en la sostenibilidad del software, poco se sabe aún sobre las prácticas específicas en la etapa de prueba para disminuir el consumo de energía. Los desarrolladores de software, a menudo, se centran en la funcionalidad y en la prueba de software sin considerar la eficiencia energética.
Objetivo: Este estudio analiza las prácticas que minimizan el consumo energético en la etapa de prueba de software, los métodos utilizados para su implementación, e identifica las herramientas de automatización que contribuyen a la reducción del consumo de energía.
Metodología: revisión sistemática de la literatura por medio del enfoque de Kitchenham. Resultados: Se analizaron 30 estudios primarios en los que se identificaron prácticas enfocadas en el ahorro de energía durante las pruebas de software. Se destacan la optimización de recursos y el escalado dinámico de voltaje y frecuencia (DVFS) que reduce el consumo de energía al ajustar la velocidad de procesamiento en función de la demanda esperada.
Conclusiones: Este trabajo proporciona una base de referencia para interesados en el desarrollo de software que busquen estrategias sustentables durante la fase de prueba.
Abstract (en)
Context: Energy consumption is important for current technological development, not only due to its economic impact but also because of its environmental implications. In software development, consumption intensifies in stages with higher energy demand, with testing being one of the most notable. Although new guidelines focused on software sustainability have been established, little is yet known about specific practices in the testing stage to reduce energy consumption. Software developers often focus on functionality and software testing without considering energy efficiency.
Objective: This study analyzes the practices that minimize energy consumption in the software testing stage, the methods used for their implementation, and identifies automation tools that contribute to reducing energy consumption. Methodology: Systematic literature review using the Kitchenham approach.
Results: Thirty primary studies were analyzed, identifying practices focused on energy savings during software testing. Notable among them are resource optimization and Dynamic Voltage and Frequency Scaling (DVFS), which reduces energy consumption by adjusting processing speed based on expected demand.
Conclusions: This work provides a baseline reference for stakeholders in software development seeking sustainable strategies during the testing phase.
References
[1] H. Münzel, "Towards an ethical foundation of green software engineering," in Proc. 10th IEEE Int. Conf. Global Softw. Eng. Workshops (ICGSEW), Ciudad Real, Spain, 2015, pp. 23–26. https://doi.org/10.1109/ICGSEW.2015.17 ↑
[2] S. Murugesan, "Harnessing green IT: Principles and practices," in Green IT: Technologies and Applications. Berlin, Germany: Springer, 2012, pp. 3–26. https://doi.org/10.1109/MITP.2008.10 ↑
[3] E. Jagroep et al., "Awakening awareness on energy consumption in software engineering," in Proc. 39th IEEE/ACM Int. Conf. Softw. Eng.: Softw. Eng. Soc. Track (ICSE-SEIS), Buenos Aires, Argentina, 2017, pp. 76–85. https://doi.org/10.1109/ICSE-SEIS.2017.10↑
[4] K. J. Valle-Gómez, P. Delgado-Pérez, I. Medina-Bulo, and J. Magallanes-Fernández, "Software testing: Cost reduction in Industry 4.0," in Proc. 14th IEEE/ACM Int. Workshop Autom. Softw. Test (AST), Montreal, QC, Canada, 2019, pp. 69–70. https://doi.org/10.1109/AST.2019.00018↑
[5] I. Manotas et al., "An empirical study of practitioners' perspectives on green software engineering," in Proc. 38th IEEE/ACM Int. Conf. Softw. Eng. (ICSE), Austin, TX, USA, 2016, pp. 237–248. https://doi.org/10.1145/2884781.2884810 ↑
[6] S. Asadi, A. R. C. Hussin, and H. M. Dahlan, "Organizational research in the field of green IT: A systematic literature review from 2007 to 2016," Telemat. Inform., vol. 34, no. 7, pp. 1191–1249, Nov. 2017. https://doi.org/10.1016/j.tele.2017.05.009 ↑
[7] R. Jabbarvand and S. Malek, "Advancing energy testing of mobile applications," in Proc. 39th IEEE/ACM Int. Conf. Softw. Eng. Companion (ICSE-C), Buenos Aires, Argentina, 2017, pp. 491–492. https://doi.org/10.1109/ICSE-C.2017.45 ↑
[8] B. R. Bruce, J. Petke, and M. Harman, "Reducing energy consumption using genetic improvement," in Proc. 2015 Annu. Conf. Genetic Evol. Comput. (GECCO '15), New York, NY, USA: ACM, 2015, pp. 1327–1334. https://doi.org/10.1145/2739480.2754752↑
[9] A. C. Moises, A. Malucelli, and S. Reinehr, "Prácticas de consumo energético para la ingeniería de software sostenible," in Proc. 9th Int. Green Sustain. Comput. Conf. (IGSC), Pittsburgh, PA, USA, 2018, pp. 1–6. https://doi.org/10.1109/IGCC.2018.8752151 ↑
[10] B. A. Kitchenham, D. Budgen, and P. Brereton, Evidence-Based Software Engineering and Systematic Reviews, vol. 4. Boca Raton, FL, USA: CRC Press, 2015. https://doi.org/10.5555/2994449↑
[11] H. Zhang, M. A. Babar, and P. Tell, "Identifying relevant studies in software engineering," Inf. Softw. Technol., vol. 53, no. 6, pp. 625–637, Jun. 2011. https://doi.org/10.1016/j.infsof.2010.12.010↑
[12] J. Popay et al., "Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC Methods Programme," ESRC Methods Programme, Technical Report, 2006. https://doi.org/10.13140/2.1.1018.4643 ↑
[13] C. Wohlin, "Guidelines for snowballing in systematic literature studies and a replication in software engineering," in Proc. 18th Int. Conf. Eval. Assessment Softw. Eng. (EASE '14), New York, NY, USA: ACM, 2014. https://doi.org/10.1145/2601248.2601268↑
[14] F. Wedyan, R. Morrison, and O. S. Abuomar, "Integration and unit testing of software energy consumption," in Proc. 10th Int. Conf. Softw. Defined Syst. (SDS), San Antonio, TX, USA, 2023, pp. 60–64. https://doi.org/10.1109/SDS59856.2023.10329262 ↑
[15] G. Gharachorlu and N. Sumner, "Avoiding the familiar to speed up test case reduction," in Proc. IEEE Int. Conf. Softw. Quality, Reliab. Secur. (QRS), Lisbon, Portugal, 2018, pp. 426–437. https://doi.org/10.1109/QRS.2018.00056 ↑
[16] E. Y. Y. Kan, "Energy efficiency in testing and regression testing — A comparison of DVFS techniques," in Proc. 13th Int. Conf. Quality Softw. (QSIC), Nanjing, China, 2013, pp. 280–283. https://doi.org/10.1109/QSIC.2013.21 ↑
[17] S. Godboley, A. Dutta, B. Besra, and D. P. Mohapatra, "Green-JEXJ: A new tool to measure energy consumption of improved concolic testing," in Proc. Int. Conf. Green Comput. Internet of Things (ICGCIoT), Greater Noida, India, 2015, pp. 36–40. https://doi.org/10.1109/ICGCIoT.2015.7380424↑
[18] R. Verdecchia, P. Lago, C. Ebert, and C. de Vries, "Green IT and green software," IEEE Softw., vol. 38, no. 6, pp. 7–15, Nov.–Dec. 2021. https://doi.org/10.1109/MS.2021.3102254 ↑
[19] D. Di Nucci, A. Panichella, A. Zaidman, and A. De Lucia, "Hypervolume-based search for test case prioritization," in Search-Based Software Engineering (SSBSE 2015), Lecture Notes in Computer Science, vol. 9275, M. Barros and Y. Labiche, Eds. Cham, Switzerland: Springer, 2015. https://doi.org/10.1007/978-3-319-22183-0_11↑
[20] G. Gharachorlu and N. Sumner, "Pardis: Priority aware test case reduction," in Fundamental Approaches to Software Engineering (FASE 2019), Lecture Notes in Computer Science, vol. 11424, R. Hähnle and W. van der Aalst, Eds. Cham, Switzerland: Springer, 2019. https://doi.org/10.1007/978-3-030-16722-6_24 ↑
[21] M. Dick, J. Drangmeister, E. Kern, and S. Naumann, "Green software engineering with agile methods," in Proc. 2nd Int. Workshop Green Sustain. Softw. (GREENS), San Francisco, CA, USA, 2013, pp. 78–85. https://doi.org/10.1109/GREENS.2013.6606425 ↑
[22] J. Larsson and E. P. Enoiu, "Test generation and mutation analysis of energy consumption using UPPAAL SMC and MATS," in Proc. IEEE Int. Conf. Softw. Test. Verif. Validation Workshops (ICSTW), Dublin, Ireland, 2023, pp. 186–189. https://doi.org/10.1109/ICSTW58534.2023.00042 ↑
[23] Md. R. H. Misu, J. Li, A. Bhattiprolu, Y. Liu, E. S. de Almeida, and I. Ahmed, "Test smell: A parasitic energy consumer in software testing," Inf. Softw. Technol., vol. 181, art. 107671, May 2025. https://doi.org/10.1016/j.infsof.2025.107671 ↑
[24] C. Camacho, S. Marczak, and T. Conte, "On the identification of best practices for improving the efficiency of testing activities in distributed software projects: Preliminary findings from an empirical study," in Proc. 8th IEEE Int. Conf. Global Softw. Eng. Workshops (ICGSEW), Bari, Italy, 2013, pp. 1–4. https://doi.org/10.1109/ICGSEW.2013.7 ↑
[25] M. A. Beghoura, A. Boubetra, and A. Boukerram, "Green software requirements and measurement: Random decision forests-based software energy consumption profiling," Requir. Eng., vol. 22, no. 1, pp. 27–40, Mar. 2017. https://doi.org/10.1007/s00766-015-0234-2 ↑
[26] A. Hindle, "Green mining: A methodology of relating software change and configuration to power consumption," Empir. Softw. Eng., vol. 20, pp. 374–409, 2015. https://doi.org/10.1007/s10664-013-9276-6 ↑
[27] A. Zaidman, "An inconvenient truth in software engineering? The environmental impact of testing open source Java projects," in Proc. IEEE/ACM Int. Conf. Autom. Softw. Test (AST), Lisbon, Portugal, 2024, pp. 214–218. https://doi.org/10.1145/3644032.3644461 ↑
[28] S. Godboley, S. Panda, A. Dutta et al., "An automated analysis of the branch coverage and energy consumption using concolic testing," Arab. J. Sci. Eng., vol. 42, pp. 619–637, 2017. https://doi.org/10.1007/s13369-016-2284-2 ↑
[29] S. Herfert, J. Patra, and M. Pradel, "Automatically reducing tree-structured test inputs," in Proc. 32nd IEEE/ACM Int. Conf. Autom. Softw. Eng. (ASE), Urbana, IL, USA, 2017, pp. 861–871. https://doi.org/10.1109/ASE.2017.8115697 ↑
[30] D. Li, C. Sahin, J. Clause, and W. G. J. Halfond, "Energy-directed test suite optimization," in Proc. 2nd Int. Workshop Green Sustain. Softw. (GREENS), San Francisco, CA, USA, 2013, pp. 62–69. https://doi.org/10.1109/GREENS.2013.6606423↑
[31] L. V. Povoa, P. W. Bignatto, C. E. Monteiro, D. Mueller, C. A. C. Marcondes, and H. Senger, "A model for estimating energy consumption based on resources utilization," in Proc. IEEE Symp. Comput. Commun. (ISCC), Split, Croatia, 2013, pp. 1–6. https://doi.org/10.1109/ISCC.2013.6754957↑
[32] S. Godboley, A. Dutta, and D. P. Mohapatra, "Reduced energy consumption for MC/DC testing," Int. J. Bus. Inf. Syst., vol. 28, no. 4, pp. 447–467, 2018. https://doi.org/10.1504/IJBIS.2018.093657 ↑
[33] D. Di Nucci, "Methods and tools for focusing and prioritizing the testing effort," in Proc. IEEE Int. Conf. Softw. Maintenance Evol. (ICSME), Madrid, Spain, 2018, pp. 722–726. https://doi.org/10.1109/ICSME.2018.00089 ↑
[34] R. Jabbarvand, A. Sadeghi, H. Bagheri, and S. Malek, "Energy-aware test-suite minimization for Android apps," in Proc. 25th Int. Symp. Softw. Test. Analysis (ISSTA 2016), New York, NY, USA: ACM, 2016, pp. 425–436. https://doi.org/10.1145/2931037.2931067 ↑
[35] Sahar Tahvili, Mehrdad Saadatmand, and M. Bohlin, “Multi-Criteria Test Case Prioritization Using Fuzzy Analytic Hierarchy Process,” ResearchGate, Nov. 15, 2015. https://www.researchgate.net/publication/281593743_Multi-Criteria_Test_Case_Prioritization_Using_Fuzzy_Analytic_Hierarchy_Process (accessed May 28, 2026)
[36] J.-W. Lin, R. Jabbarvand, J. Garcia, and S. Malek, "Nemo: Multi-criteria test-suite minimization with integer nonlinear programming," in Proc. 40th IEEE/ACM Int. Conf. Softw. Eng. (ICSE), Gothenburg, Sweden, 2018, pp. 1039–1049. https://doi.org/10.1145/3180155.3180174 ↑
[37] D. Di Nucci, F. Palomba, A. Prota, A. Panichella, A. Zaidman, and A. De Lucia, "PETRA: A software-based tool for estimating the energy profile of Android applications," in Proc. 39th IEEE/ACM Int. Conf. Softw. Eng. Companion (ICSE-C), Buenos Aires, Argentina, 2017, pp. 3–6. https://doi.org/10.1109/ICSE-C.2017.18 ↑
[38] S. Song, F. Wedyan, and Y. Jararweh, "Empirical evaluation of energy consumption for mobile applications," in Proc. 12th Int. Conf. Inf. Commun. Syst. (ICICS), Valencia, Spain, 2021, pp. 352–357. https://doi.org/10.1109/ICICS52457.2021.9464579 ↑
[39] M. Mohankumar and M. Anand Kumar, "An empirical study on green and sustainable software engineering," 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:36428799. ↑
[40] A. Dutta, "Green-J3 model: A novel approach to measure energy consumption of modified condition/decision coverage using concolic testing," CSI Trans. ICT, 2017. https://doi.org/10.1007/S40012-017-0157-9 ↑
[41] D. Li, Y. Jin, C. Sahin, J. Clause, and W. G. J. Halfond, "Integrated energy-directed test suite optimization," in Proc. Int. Symp. Softw. Test. Analysis (ISSTA 2014), New York, NY, USA: ACM, 2014, pp. 339–350. https://doi.org/10.1145/2610384.2610414↑
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Copyright (c) 2026 Eduardo López-Chacón, Juan Carlos Pérez-Arriaga, Ángel J. Sánchez-García, Lizbeth Alejandra Hernández-González

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