What Do Programmers Know about Software Energy Consumption?

Pang, Candy; Hindle, Abram; Adams, Bram; Hassan, Ahmed E.

doi:10.1109/ms.2015.83

Cited by 118 publications

(55 citation statements)

References 7 publications

(15 reference statements)

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“…In recent years, there has been an increase in the number of empirical studies on developer discussions. In general, these studies are conducted using online surveys [10], interviews [11], and mining questions and answers on online discussion forums [15, 16, 23]. Off late, online repositories such as Stack Overflow have proven to be a reliable source to summarise a developer's knowledge [13, 24].…”

Section: Related Workmentioning

confidence: 99%

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Organising the knowledge from stack overflow about location‐sensing of Android applications

2020

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The number of Android applications using location information has increased significantly in recent years. Over time, there have been many improvements made to the location application programme interfaces (APIs), providing newer challenges and difficulties to the developers. Therefore, there is a need to summarise the existing knowledge and to highlight the unsolved issues to bring them to the attention of expert developers. The authors used the non-negative matrix factorisation (NMF) method to identify the topics discussed by the developers on stack overflow. They found the following ten topics: fundamental, background service, global positioning system (GPS) provider, application error, location updates, programming aspects, GPS alternatives, location settings, NULL location, and location testing. In addition, they performed a manual analysis to add more qualitative insights into the results. They applied the NMF method on 3165 question posts and produced ten related topics. This study aims at organising the knowledge about location-sensing strategies by answering three relevant research questions. They also analysed the most popular and unanswered topics in recent years. An important finding of this study is that the changes that occurred in the Google Location APIs have had a significant impact on the location-sensing strategies followed by the developers.

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Section: Related Workmentioning

confidence: 99%

“…In general, mobile development teams are relatively small and consist of developers with less background knowledge [10, 11] on best practices of using location APIs. Therefore, the new developers post their issues on developer discussion forums such as Stack Overflow to get suitable and working solutions [12].…”

Section: Introductionmentioning

confidence: 99%

Organising the knowledge from stack overflow about location‐sensing of Android applications

2020

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“…These switches/v‐switches 87,88 run a specific protocol to enable communication, eg, OpenFlow, 89 network configuration protocol (NETCONF), 90,91 border gateway protocol (BGP), 92 Extensible Messaging, and Presence Protocol (XMPP) 93 . In addition, virtual and physical devices, 94 cooling requirements, 14, 95–97 resilience, 31, 98–99 HCI, and DCIM 100–103 are also facilitators of communication that took place between green controller and this plane. Data or forwarding plane of SDN comprises of a set of components that can be green routers, green switches, firewalls, networking virtual appliances, and so forth 104 .…”

Section: Proposed Framework Of Green Sdn (G‐sdn)mentioning

confidence: 99%

“…Similarly, energy consumption becomes a non‐functional requirement for all the programmers and the data centers. As a result, industries are trying to reduce the energy consumption through virtualization and data center infrastructure management (DCIM) systems 100–103 . It effectively manages and integrates all the technologies in the IT discipline and their quality of service is ensured by the data centers 100–102 .…”

Section: Proposed Framework Of Green Sdn (G‐sdn)mentioning

confidence: 99%

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

Moin

Karim

Safdar

et al. 2018

Concurrency and Computation

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Network architectures nowadays are not able to address the requirements of future Internet, as they are not sufficient, ossified, and are power hungry on the TCP/IP paradigm. Therefore, to promote the realizable progression toward the new paradigms and modern protocols, modern routers are required to become programmable for the flexible control and dynamic management of both power states and traffic flows. In this scenario, Software Defined Networking (SDN) is considered as an exemplary approach for providing the support of dynamic traffic flows of each node on a network. Besides the benefits that SDN brought to the networking area, there are still crucial challenges such as to design an effective, lightweight API, rich algorithms, and traffic forwarding rules for SDN layers along with the adoption of an energy efficient infrastructure that could have the least effect on the environment. The aim of this research is to drive the attention toward environment friendly adoption and implementation of SDN that could deal with the growing number of users and network equipment. For this purpose, in this research, a comprehensive review on SDN while mapping the characteristics of green computing with SDN is provided. In addition to that, the research stressed on the benefits of Green SDN through a proposed G-SDN framework. Some well-defined case studies are presented to highlight the need of greening an SDN infrastructure. Finally, various research directions are presented to the wide adoption of SDN technology for academia and industry alike. KEYWORDS green computing, green SDN, SDN, software defined networks 1 INTRODUCTION Software Defined Networking (SDN) offers a high level of flexibility, controllability, and programmability in networks with the power of centralized control. The centralized controller at SDN faces a significant performance drawback when dealing with huge network flows. 1 On the other hand, features provided by SDN allows an ease in deploying the strategies that are designed to overcome the existing problems of power and energy-waste. 2 Green computing refers to the reduction of hazard and toxic materials, improved efficiency of energy including recycling of factory wastage, 3 and it has become an integral part to enhance the reliability of each emerging computing domains. Even the cloud and green computing are applicable side by side to make greener internet and enhance services. The former techniques allow the services with ease when required at a cost of complex infrastructure. However, the later approach has edge in providing all of these services with efficient energy usage, recyclability, low environmental impact, reduced carbon emission, and much more facilities. 4 In addition, to provide the promising services to the intended users, wired and wireless networks 5,6 have been followed by many directions in prior studies 5-7 such as the closing of forward and feedback loops. 8 In this way, there exists a variety of designs such as low-power solutions with slow and fast dynamics. Solutions with sl...

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“…Results in [4] show that 18% of developers would take energy consumption into account when developing software, and 14% would consider minimizing energy consumption as a requirement during development. Thus it is critical to identify the energy bugs during the development phase so that developers are aware of the changes in their source code might cause severe problems on the energy consumption of the application.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Approaches for Android Energy Bugs Detection With Revision Commits

Zhu

Xie

et al. 2019

IEEE Access

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Performances of smartphones are profoundly affected by battery life. Maximizing the amount of usage of energy is essential to extend battery life. However, developers might concentrate more on the functionality of applications while ignoring the energy bugs that drain the battery during the development process. There are no quantitative approaches to detect these energy bugs introduced in this fast-paced development process. In this paper, we employ a system-call-based approach to develop a power consumption model for Android devices. Data that measure the energy consumption of mobile devices under different testing scenarios with the number of triggered system calls are utilized in the model training process. A balanced recursive feature elimination with cross-validation approach is proposed to select and rank the importance of the different system calls. Seven machine learning models are trained over the selected features with cross-validation and hyper-parameter tuning technique, where linear regression with the Lasso regularization outperforms all the other models. Then, the model is evaluated on the data set that measures the energy consumption on different revision history of the selected apps. The results show that the optimized Lasso model could detect energy bugs in the revision history of various applications. Optimization strategies are provided based on the selected features.

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What Do Programmers Know about Software Energy Consumption?

Cited by 118 publications

References 7 publications

Organising the knowledge from stack overflow about location‐sensing of Android applications

Organising the knowledge from stack overflow about location‐sensing of Android applications

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

Evaluation of Machine Learning Approaches for Android Energy Bugs Detection With Revision Commits

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