| 461. |
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| 462. |
- García Martín, Eva, et al.
(författare)
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Energy-aware very fast decision tree
- 2021
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Ingår i: International Journal of Data Science and Analytics. - : Springer. - 2364-415X .- 2364-4168. ; 11:2, s. 105-126
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Tidskriftsartikel (refereegranskat)abstract
- Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.
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| 463. |
- García Martín, Eva, 1989-
(författare)
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Energy Efficiency in Machine Learning : Approaches to Sustainable Data Stream Mining
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability.This thesis explores green machine learning, which builds on green computing and computer architecture to design sustainable and energy efficient machine learning algorithms. In particular, we investigate how to design machine learning algorithms that automatically learn from streaming data in an energy efficient manner.We first illustrate how energy can be measured in the context of machine learning, in the form of a literature review and a procedure to create theoretical energy models. We use this knowledge to analyze the energy footprint of Hoeffding trees, presenting an energy model that maps the number of computations and memory accesses to the main functionalities of the algorithm. We also analyze the hardware events correlated to the execution of the algorithm, their functions and their hyper parameters.The final contribution of the thesis is showcased by two novel extensions of Hoeffding tree algorithms, the Hoeffding tree with nmin adaptation and the Green Accelerated Hoeffding Tree. These solutions are able to reduce their energy consumption by twenty and thirty percent, with minimal effect on accuracy. This is achieved by setting an individual splitting criteria for each branch of the decision tree, spending more energy on the fast growing branches and saving energy on the rest.This thesis shows the importance of evaluating energy consumption when designing machine learning algorithms, proving that we can design more energy efficient algorithms and still achieve competitive accuracy results.
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| 464. |
- García Martín, Eva, et al.
(författare)
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Energy modeling of Hoeffding tree ensembles
- 2021
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 25:1, s. 81-104
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Tidskriftsartikel (refereegranskat)abstract
- Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.
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| 465. |
- Garousi, Vahid, et al.
(författare)
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Benefitting from the Grey Literature in Software Engineering Research
- 2020
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Ingår i: Contemporary Empirical Methods in Software Engineering. - Cham : Springer Nature. - 9783030324889 ; , s. 385-413
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Bokkapitel (refereegranskat)abstract
- Researchers generally place the most trust in peer-reviewed, published information, such as journals and conference papers. By contrast, software engineering (SE) practitioners typically do not have the time, access, or expertise to review and benefit from such publications. As a result, practitioners are more likely to turn to other sources of information that they trust, e.g., trade magazines, online blog posts, survey results, or technical reports, collectively referred to as grey literature (GL). Furthermore, practitioners also share their ideas and experiences as GL, which can serve as a valuable data source for research. While GL itself is not a new topic in SE, using, benefitting, and synthesizing knowledge from the GL in SE is a contemporary topic in empirical SE research and we are seeing that researchers are increasingly benefitting from the knowledge available within GL. The goal of this chapter is to provide an overview of GL in SE, together with insights on how SE researchers can effectively use and benefit from the knowledge and evidence available in the vast amount of GL.
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| 466. |
- Garousi, Vahid, et al.
(författare)
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Closing the Gap Between Software Engineering Education and Industrial Needs
- 2020
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Ingår i: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 7:2, s. 68-77
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Tidskriftsartikel (refereegranskat)abstract
- According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce. IEEE
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| 467. |
- Garousi, Vahid, et al.
(författare)
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Exploring the industry's challenges in software testing : An empirical study
- 2020
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Ingår i: Journal of Software. - : WILEY. - 2047-7473 .- 2047-7481. ; 32:8
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Tidskriftsartikel (refereegranskat)abstract
- Context Software testing is an important and costly software engineering activity in the industry. Despite the efforts of the software testing research community in the last several decades, various studies show that still many practitioners in the industry report challenges in their software testing tasks. Objective To shed light on industry's challenges in software testing, we characterize and synthesize the challenges reported by practitioners. Such concrete challenges can then be used for a variety of purposes, eg, research collaborations between industry and academia. Method Our empirical research method is opinion survey. By designing an online survey, we solicited practitioners' opinions about their challenges in different testing activities. Our dataset includes data from 72 practitioners from eight different countries. Results Our results show that test management and test automation are considered the most challenging among all testing activities by practitioners. Our results also include a set of 104 concrete challenges in software testing that may need further investigations by the research community. Conclusion We conclude that the focal points of industrial work and academic research in software testing differ. Furthermore, the paper at hand provides valuable insights concerning practitioners' "pain" points and, thus, provides researchers with a source of important research topics of high practical relevance.
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| 468. |
- Garousi, Vahid, et al.
(författare)
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Introduction to the Special Issue on : Grey Literature and Multivocal Literature Reviews (MLRs) in software engineering
- 2022
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Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025. ; 141
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Tidskriftsartikel (refereegranskat)abstract
- In parallel to academic (peer-reviewed) literature (e.g., journal and conference papers), an enormous extent of grey literature (GL) has accumulated since the inception of software engineering (SE). GL is often defined as “literature that is not formally published in sources such as books or journal articles”, e.g., in the form of trade magazines, online blog-posts, technical reports, and online videos such as tutorial and presentation videos. GL is typically produced by SE practitioners. We have observed that researchers are increasingly using and benefitting from the knowledge available within GL. Related to the notion of GL is the notion of Multivocal Literature Reviews (MLRs) in SE, i.e., a MLR is a form of a Systematic Literature Review (SLR) which includes knowledge and/or evidence from the GL in addition to the peer-reviewed literature. MLRs are useful for both researchers and practitioners because they provide summaries of both the state-of-the-art and -practice in a given area. MLRs are popular in other fields and have started to appear in SE community. It is timely then for a Special Issue (SI) focusing on GL and MLRs in SE. From the pool of 13 submitted papers, and after following a rigorous peer review process, seven papers were accepted for this SI. In this introduction we provide a brief overview of GL and MLRs in SE, and then a brief summary of the seven papers published in this SI. © 2021
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| 469. |
- Garousi, Vahid, et al.
(författare)
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Mining user reviews of COVID contact-tracing apps : An exploratory analysis of nine European apps
- 2022
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Ingår i: Journal of Systems and Software. - : Elsevier Inc.. - 0164-1212 .- 1873-1228. ; 184
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Tidskriftsartikel (refereegranskat)abstract
- Context: More than 78 countries have developed COVID contact-tracing apps to limit the spread of coronavirus. However, many experts and scientists cast doubt on the effectiveness of those apps. For each app, a large number of reviews have been entered by end-users in app stores. Objective: Our goal is to gain insights into the user reviews of those apps, and to find out the main problems that users have reported. Our focus is to assess the “software in society” aspects of the apps, based on user reviews. Method: We selected nine European national apps for our analysis and used a commercial app-review analytics tool to extract and mine the user reviews. For all the apps combined, our dataset includes 39,425 user reviews. Results: Results show that users are generally dissatisfied with the nine apps under study, except the Scottish (“Protect Scotland”) app. Some of the major issues that users have complained about are high battery drainage and doubts on whether apps are really working. Conclusion: Our results show that more work is needed by the stakeholders behind the apps (e.g., app developers, decision-makers, public health experts) to improve the public adoption, software quality and public perception of these apps. © 2021 Elsevier Inc.
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| 470. |
- Garousi, Vahid, et al.
(författare)
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NLP-assisted software testing : a systematic mapping of the literature
- 2020
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Ingår i: Information and Software Technology. - : Elsevier B.V.. - 0950-5849 .- 1873-6025. ; 126
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Forskningsöversikt (refereegranskat)abstract
- Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners. © 2020 Elsevier B.V.
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