What can Android mobile app developers do about the energy consumption of machine learning? View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Andrea McIntosh, Safwat Hassan, Abram Hindle

ABSTRACT

Machine learning is a popular method of learning functions from data to represent and to classify sensor inputs, multimedia, emails, and calendar events. Smartphone applications have been integrating more and more intelligence in the form of machine learning. Machine learning functionality now appears on most smartphones as voice recognition, spell checking, word disambiguation, face recognition, translation, spatial reasoning, and even natural language summarization. Excited app developers who want to use machine learning on mobile devices face one serious constraint that they did not face on desktop computers or cloud virtual machines: the end-user’s mobile device has limited battery life, thus computationally intensive tasks can harm end users’ phone availability by draining batteries of their stored energy. Currently, there are few guidelines for developers who want to employ machine learning on mobile devices yet are concerned about software energy consumption of their applications. In this paper, we combine empirical measurements of different machine learning algorithm implementations with complexity theory to provide concrete and theoretically grounded recommendations to developers who want to employ machine learning on smartphones. We conclude that some implementations of algorithms, such as J48, MLP, and SMO, do generally perform better than others in terms of energy consumption and accuracy, and that energy consumption is well-correlated to algorithmic complexity. However, to achieve optimal results a developer must consider their specific application as many factors — dataset size, number of data attributes, whether the model will require updating, etc. — affect which machine learning algorithm and implementation will provide the best results. More... »

PAGES

562-601

References to SciGraph publications

  • 1991-01. Instance-based learning algorithms in MACHINE LEARNING
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2013-12. Automated topic naming in EMPIRICAL SOFTWARE ENGINEERING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10664-018-9629-2

    DOI

    http://dx.doi.org/10.1007/s10664-018-9629-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1104379007


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Alberta", 
              "id": "https://www.grid.ac/institutes/grid.17089.37", 
              "name": [
                "Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "McIntosh", 
            "givenName": "Andrea", 
            "id": "sg:person.012755235342.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012755235342.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Queen's University", 
              "id": "https://www.grid.ac/institutes/grid.410356.5", 
              "name": [
                "Software Analysis and Intelligence Lab (SAIL), Queen\u2019s University, Kingston, Ontario, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hassan", 
            "givenName": "Safwat", 
            "id": "sg:person.013262524160.86", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013262524160.86"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Alberta", 
              "id": "https://www.grid.ac/institutes/grid.17089.37", 
              "name": [
                "Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hindle", 
            "givenName": "Abram", 
            "id": "sg:person.010203314771.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010203314771.36"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/2884781.2884867", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000779742"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2786805.2786847", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002993677"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2635868.2635871", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003965930"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2597073.2597097", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005743014"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2901739.2901763", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006610026"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2739480.2754752", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009037032"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2070562.2070567", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019103137"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/089976601300014493", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019128510"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2994551.2994564", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021136339"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2896967.2896970", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021789225"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1010933404324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024739340", 
              "https://doi.org/10.1023/a:1010933404324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10664-012-9209-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026591474", 
              "https://doi.org/10.1007/s10664-012-9209-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2568225.2568297", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028503282"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1656274.1656278", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028526411"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2896967.2896968", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029372023"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jss.2016.03.031", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031061349"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2884781.2884810", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031161487"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2593743.2593749", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035665419"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2884781.2884869", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040957765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/smr.1762", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041552834"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2597073.2597085", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041822769"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1966445.1966460", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043899937"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/860435.860455", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047967397"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00153759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049631378", 
              "https://doi.org/10.1007/bf00153759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00153759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049631378", 
              "https://doi.org/10.1007/bf00153759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2597073.2597110", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052599881"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2010.36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662273"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jss.2017.04.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085056457"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/stvr.1635", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085993794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/msr.2015.53", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093380917"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icsm.2015.7332477", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093853624"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icsme.2014.34", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093970624"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ijcnn.2001.939044", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094359545"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icsme.2014.35", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094656290"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/saner.2017.7884613", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095009493"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/saner.2016.64", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095083870"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/saner.2016.77", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095424671"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/greens.2015.9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095443801"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mobilesoft.2017.17", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095601048"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icse.2015.32", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095690368"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "Machine learning is a popular method of learning functions from data to represent and to classify sensor inputs, multimedia, emails, and calendar events. Smartphone applications have been integrating more and more intelligence in the form of machine learning. Machine learning functionality now appears on most smartphones as voice recognition, spell checking, word disambiguation, face recognition, translation, spatial reasoning, and even natural language summarization. Excited app developers who want to use machine learning on mobile devices face one serious constraint that they did not face on desktop computers or cloud virtual machines: the end-user\u2019s mobile device has limited battery life, thus computationally intensive tasks can harm end users\u2019 phone availability by draining batteries of their stored energy. Currently, there are few guidelines for developers who want to employ machine learning on mobile devices yet are concerned about software energy consumption of their applications. In this paper, we combine empirical measurements of different machine learning algorithm implementations with complexity theory to provide concrete and theoretically grounded recommendations to developers who want to employ machine learning on smartphones. We conclude that some implementations of algorithms, such as J48, MLP, and SMO, do generally perform better than others in terms of energy consumption and accuracy, and that energy consumption is well-correlated to algorithmic complexity. However, to achieve optimal results a developer must consider their specific application as many factors \u2014 dataset size, number of data attributes, whether the model will require updating, etc. \u2014 affect which machine learning algorithm and implementation will provide the best results.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10664-018-9629-2", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1136797", 
            "issn": [
              "1382-3256", 
              "1573-7616"
            ], 
            "name": "Empirical Software Engineering", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "24"
          }
        ], 
        "name": "What can Android mobile app developers do about the energy consumption of machine learning?", 
        "pagination": "562-601", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "82a5f37c787cf949300bce82e59c9c5541b07daf2216869e099ce3f49353d77c"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10664-018-9629-2"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1104379007"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10664-018-9629-2", 
          "https://app.dimensions.ai/details/publication/pub.1104379007"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:25", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000369_0000000369/records_68973_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10664-018-9629-2"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10664-018-9629-2'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10664-018-9629-2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10664-018-9629-2'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10664-018-9629-2'


     

    This table displays all metadata directly associated to this object as RDF triples.

    198 TRIPLES      21 PREDICATES      66 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10664-018-9629-2 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N696f14b363ef4b6c81d3fc90256a927d
    4 schema:citation sg:pub.10.1007/bf00153759
    5 sg:pub.10.1007/s10664-012-9209-9
    6 sg:pub.10.1023/a:1010933404324
    7 https://doi.org/10.1002/smr.1762
    8 https://doi.org/10.1002/stvr.1635
    9 https://doi.org/10.1016/j.jss.2016.03.031
    10 https://doi.org/10.1016/j.jss.2017.04.018
    11 https://doi.org/10.1109/greens.2015.9
    12 https://doi.org/10.1109/icse.2015.32
    13 https://doi.org/10.1109/icsm.2015.7332477
    14 https://doi.org/10.1109/icsme.2014.34
    15 https://doi.org/10.1109/icsme.2014.35
    16 https://doi.org/10.1109/ijcnn.2001.939044
    17 https://doi.org/10.1109/mobilesoft.2017.17
    18 https://doi.org/10.1109/msr.2015.53
    19 https://doi.org/10.1109/saner.2016.64
    20 https://doi.org/10.1109/saner.2016.77
    21 https://doi.org/10.1109/saner.2017.7884613
    22 https://doi.org/10.1109/tkde.2010.36
    23 https://doi.org/10.1145/1656274.1656278
    24 https://doi.org/10.1145/1966445.1966460
    25 https://doi.org/10.1145/2070562.2070567
    26 https://doi.org/10.1145/2568225.2568297
    27 https://doi.org/10.1145/2593743.2593749
    28 https://doi.org/10.1145/2597073.2597085
    29 https://doi.org/10.1145/2597073.2597097
    30 https://doi.org/10.1145/2597073.2597110
    31 https://doi.org/10.1145/2635868.2635871
    32 https://doi.org/10.1145/2739480.2754752
    33 https://doi.org/10.1145/2786805.2786847
    34 https://doi.org/10.1145/2884781.2884810
    35 https://doi.org/10.1145/2884781.2884867
    36 https://doi.org/10.1145/2884781.2884869
    37 https://doi.org/10.1145/2896967.2896968
    38 https://doi.org/10.1145/2896967.2896970
    39 https://doi.org/10.1145/2901739.2901763
    40 https://doi.org/10.1145/2994551.2994564
    41 https://doi.org/10.1145/860435.860455
    42 https://doi.org/10.1162/089976601300014493
    43 schema:datePublished 2019-04
    44 schema:datePublishedReg 2019-04-01
    45 schema:description Machine learning is a popular method of learning functions from data to represent and to classify sensor inputs, multimedia, emails, and calendar events. Smartphone applications have been integrating more and more intelligence in the form of machine learning. Machine learning functionality now appears on most smartphones as voice recognition, spell checking, word disambiguation, face recognition, translation, spatial reasoning, and even natural language summarization. Excited app developers who want to use machine learning on mobile devices face one serious constraint that they did not face on desktop computers or cloud virtual machines: the end-user’s mobile device has limited battery life, thus computationally intensive tasks can harm end users’ phone availability by draining batteries of their stored energy. Currently, there are few guidelines for developers who want to employ machine learning on mobile devices yet are concerned about software energy consumption of their applications. In this paper, we combine empirical measurements of different machine learning algorithm implementations with complexity theory to provide concrete and theoretically grounded recommendations to developers who want to employ machine learning on smartphones. We conclude that some implementations of algorithms, such as J48, MLP, and SMO, do generally perform better than others in terms of energy consumption and accuracy, and that energy consumption is well-correlated to algorithmic complexity. However, to achieve optimal results a developer must consider their specific application as many factors — dataset size, number of data attributes, whether the model will require updating, etc. — affect which machine learning algorithm and implementation will provide the best results.
    46 schema:genre research_article
    47 schema:inLanguage en
    48 schema:isAccessibleForFree false
    49 schema:isPartOf N0fa48a8d6564464f8533213ed5481ba7
    50 N410ca722a4624286b085a96004bb3c3d
    51 sg:journal.1136797
    52 schema:name What can Android mobile app developers do about the energy consumption of machine learning?
    53 schema:pagination 562-601
    54 schema:productId N321241949ea74744ae9ee59b51586572
    55 N45377ae8f36a44b0b0f9170614da3a34
    56 N8167b4c0579c4c9f8a00032ea62b1a5d
    57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104379007
    58 https://doi.org/10.1007/s10664-018-9629-2
    59 schema:sdDatePublished 2019-04-11T13:25
    60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    61 schema:sdPublisher Na8cdc5b9987a4dcbadb613fd319cd283
    62 schema:url https://link.springer.com/10.1007%2Fs10664-018-9629-2
    63 sgo:license sg:explorer/license/
    64 sgo:sdDataset articles
    65 rdf:type schema:ScholarlyArticle
    66 N0fa48a8d6564464f8533213ed5481ba7 schema:volumeNumber 24
    67 rdf:type schema:PublicationVolume
    68 N321241949ea74744ae9ee59b51586572 schema:name dimensions_id
    69 schema:value pub.1104379007
    70 rdf:type schema:PropertyValue
    71 N410ca722a4624286b085a96004bb3c3d schema:issueNumber 2
    72 rdf:type schema:PublicationIssue
    73 N45377ae8f36a44b0b0f9170614da3a34 schema:name doi
    74 schema:value 10.1007/s10664-018-9629-2
    75 rdf:type schema:PropertyValue
    76 N696f14b363ef4b6c81d3fc90256a927d rdf:first sg:person.012755235342.02
    77 rdf:rest N7191b7984ed844a79f19c252f568d23d
    78 N7191b7984ed844a79f19c252f568d23d rdf:first sg:person.013262524160.86
    79 rdf:rest N7db8f7133bc949df9e2d59c55ec5c20a
    80 N7db8f7133bc949df9e2d59c55ec5c20a rdf:first sg:person.010203314771.36
    81 rdf:rest rdf:nil
    82 N8167b4c0579c4c9f8a00032ea62b1a5d schema:name readcube_id
    83 schema:value 82a5f37c787cf949300bce82e59c9c5541b07daf2216869e099ce3f49353d77c
    84 rdf:type schema:PropertyValue
    85 Na8cdc5b9987a4dcbadb613fd319cd283 schema:name Springer Nature - SN SciGraph project
    86 rdf:type schema:Organization
    87 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    88 schema:name Information and Computing Sciences
    89 rdf:type schema:DefinedTerm
    90 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    91 schema:name Artificial Intelligence and Image Processing
    92 rdf:type schema:DefinedTerm
    93 sg:journal.1136797 schema:issn 1382-3256
    94 1573-7616
    95 schema:name Empirical Software Engineering
    96 rdf:type schema:Periodical
    97 sg:person.010203314771.36 schema:affiliation https://www.grid.ac/institutes/grid.17089.37
    98 schema:familyName Hindle
    99 schema:givenName Abram
    100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010203314771.36
    101 rdf:type schema:Person
    102 sg:person.012755235342.02 schema:affiliation https://www.grid.ac/institutes/grid.17089.37
    103 schema:familyName McIntosh
    104 schema:givenName Andrea
    105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012755235342.02
    106 rdf:type schema:Person
    107 sg:person.013262524160.86 schema:affiliation https://www.grid.ac/institutes/grid.410356.5
    108 schema:familyName Hassan
    109 schema:givenName Safwat
    110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013262524160.86
    111 rdf:type schema:Person
    112 sg:pub.10.1007/bf00153759 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049631378
    113 https://doi.org/10.1007/bf00153759
    114 rdf:type schema:CreativeWork
    115 sg:pub.10.1007/s10664-012-9209-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026591474
    116 https://doi.org/10.1007/s10664-012-9209-9
    117 rdf:type schema:CreativeWork
    118 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
    119 https://doi.org/10.1023/a:1010933404324
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1002/smr.1762 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041552834
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1002/stvr.1635 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085993794
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1016/j.jss.2016.03.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031061349
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1016/j.jss.2017.04.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085056457
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1109/greens.2015.9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095443801
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1109/icse.2015.32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095690368
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1109/icsm.2015.7332477 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093853624
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1109/icsme.2014.34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093970624
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1109/icsme.2014.35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094656290
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1109/ijcnn.2001.939044 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094359545
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1109/mobilesoft.2017.17 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095601048
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1109/msr.2015.53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093380917
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1109/saner.2016.64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095083870
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1109/saner.2016.77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095424671
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/saner.2017.7884613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095009493
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/tkde.2010.36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662273
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1145/1656274.1656278 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028526411
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1145/1966445.1966460 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043899937
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1145/2070562.2070567 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019103137
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1145/2568225.2568297 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028503282
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1145/2593743.2593749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035665419
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1145/2597073.2597085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041822769
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1145/2597073.2597097 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005743014
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1145/2597073.2597110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052599881
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1145/2635868.2635871 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003965930
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1145/2739480.2754752 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009037032
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1145/2786805.2786847 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002993677
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1145/2884781.2884810 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031161487
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1145/2884781.2884867 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000779742
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1145/2884781.2884869 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040957765
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1145/2896967.2896968 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029372023
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1145/2896967.2896970 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021789225
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1145/2901739.2901763 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006610026
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1145/2994551.2994564 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021136339
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1145/860435.860455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047967397
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1162/089976601300014493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019128510
    192 rdf:type schema:CreativeWork
    193 https://www.grid.ac/institutes/grid.17089.37 schema:alternateName University of Alberta
    194 schema:name Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
    195 rdf:type schema:Organization
    196 https://www.grid.ac/institutes/grid.410356.5 schema:alternateName Queen's University
    197 schema:name Software Analysis and Intelligence Lab (SAIL), Queen’s University, Kingston, Ontario, Canada
    198 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...