{"id":619,"date":"2021-02-05T07:48:42","date_gmt":"2021-02-05T07:48:42","guid":{"rendered":"https:\/\/metrics.blogg.gu.se\/?p=619"},"modified":"2021-01-09T18:49:25","modified_gmt":"2021-01-09T18:49:25","slug":"crowdsmelling","status":"publish","type":"post","link":"https:\/\/metrics.blogg.gu.se\/?p=619","title":{"rendered":"Crowdsmelling\u2026"},"content":{"rendered":"\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"678\" src=\"https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-1024x678.jpg\" alt=\"\" class=\"wp-image-620\" srcset=\"https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-1024x678.jpg 1024w, https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-300x199.jpg 300w, https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-768x508.jpg 768w, https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-1200x794.jpg 1200w, https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920-1320x874.jpg 1320w, https:\/\/metrics.blogg.gu.se\/files\/2021\/01\/pepper-1080675_1920.jpg 1920w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 1362px) 62vw, 840px\" \/><figcaption>BIld av <a href=\"https:\/\/pixabay.com\/sv\/users\/ajale-1481387\/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=1080675\">Ajale<\/a> fr\u00e5n <a href=\"https:\/\/pixabay.com\/sv\/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=1080675\">Pixabay<\/a><\/figcaption><\/figure>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2012.12590.pdf\">2012.12590.pdf (arxiv.org)<\/a><\/p>\n\n\n\n<p>The\nconcept of crowdsourcing is well known in our community. We are accustomed to\nreading other&#8217;s code and learning from it at the same time improving it. Even\nthe &#8220;captcha&#8217;s&#8221; are a good example of crowdsourcing. <\/p>\n\n\n\n<p>However,\ncrowdsmelling? Well, the idea is not as outrageous as one might think. It&#8217;s\nactually an interesting one. It is essentially a way of using collective\nknowledge about code smells to design machine learning to recognize them. It&#8217;s\nactually the very idea which we use in our Software Center project, and which\nwe support. <\/p>\n\n\n\n<p>In this\npaper, the authors focus on special kind of code smells &#8211; the ones linked to\ntechnical debt. The results are promising and we should keep an eye on this\nwork in order to see if this improves. <\/p>\n\n\n\n<p>From the abstract: &#8220;Good performances were obtained for God Class detection (ROC=0.896 for\nNaive Bayes) and Long Method detection (ROC=0.870 for AdaBoostM1), but much\nlower for Feature Envy (ROC=0.570 for Random Forrest).&#8221;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>2012.12590.pdf (arxiv.org) The concept of crowdsourcing is well known in our community. We are accustomed to reading other&#8217;s code and learning from it at the same time improving it. Even the &#8220;captcha&#8217;s&#8221; are a good example of crowdsourcing. However, crowdsmelling? Well, the idea is not as outrageous as one might think. It&#8217;s actually an interesting &hellip; <a href=\"https:\/\/metrics.blogg.gu.se\/?p=619\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Crowdsmelling\u2026&#8221;<\/span><\/a><\/p>\n","protected":false},"author":68,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,9],"tags":[],"_links":{"self":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/619"}],"collection":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/users\/68"}],"replies":[{"embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=619"}],"version-history":[{"count":1,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/619\/revisions"}],"predecessor-version":[{"id":621,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=\/wp\/v2\/posts\/619\/revisions\/621"}],"wp:attachment":[{"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=619"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=619"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metrics.blogg.gu.se\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}