Change in Abstract

Author: aiMojica10

src/pages/MainPage/index.tsx

@@ -39,7 +39,7 @@ export const MainPage: React.FC<Props> = ({data}) => {
           <Typography variant="overline" align="center"> This is the online appendix of </Typography>
           <Typography variant="h3" align="center" margin={10}>What are the Machine Learning best practices reported by practitioners on Stack Exchange?</Typography>
           <br/>
-          <Typography variant="body1" align="justify"> <b>ABSTRACT.</b> Machine Learning (ML) is being used in multiple disciplines due to its powerful capability to infer relationships within data. In particular, Software Engineering (SE) is one of those disciplines in which ML has been used for multiple tasks, like software categorization, bugs prediction, and testing. In addition to the multiple ML applications, some studies have been conducted to detect and understand possible pitfalls and issues when using ML. However, to the best of our knowledge, only a few studies have focused on presenting ML best practices or guidelines for the application of ML in different domains. In addition, the practices and literature presented in previous literature (i) are domain-specific (e.g., concrete practices in biomechan- ics), (ii) describe few practices, or (iii) the practices lack rigorous validation and are presented in gray literature. In this paper, we present a study listing 127 ML best practices systematically mining 242 posts of 14 dfferent Stack Exchange (STE) websites and validated by four independent ML experts. The list of practices is pre- sented in a set of categories related to dierent stages of the implementation process of an ML-enabled system; for each practice, we include explanations and examples. In all the practices, the provided examples focus on SE tasks. <strong><em> We expect this list of practices could help practitioners to understand better the practices and use ML in a more informed way, in particular newcomers to this new area that sits at the intersection of software engineering and machine learning</em></strong>. </Typography>
+          <Typography variant="body1" align="justify"> <b>ABSTRACT.</b>  Machine Learning (ML) is being used in many disciplines due to its powerful capability to infer relationships within data. In particular, Software Engineering is one of those disciplines where ML has been used for various tasks, like software categorization and bug prediction. In addition to the many ML applications, some studies have been conducted to detect and understand possible pitfalls and issues when using ML. However, to the best of our knowledge, few studies have focused on presenting ML best practices or guidelines for the application of ML in different domains. Moreover, the practices and literature presented in previous literature i) are domain-specific (e.g., concrete practices in biomechanics), ii) describe few practices, or iii) the practices lack rigorous validation and are presented in gray literature. In this paper, we present a discussion about 127 identified ML best practices that were systematically mined from 242 posts of 14 different Stack Exchange websites and validated by four independent ML experts. The discussion includes an analysis of the topics covered by the practices, a comparison of key points with the state of the art, and possible reasons why the same topics are not covered in different studies. This discussion is presented in a set of categories related to different stages of the implementation process of an ML-enabled system. This study also presents an analysis of the opinions of ML experts. In addition, in our appendices, we present the details of the 127 practices, including the experts’ comments. <strong><em> We expect that this study could help practitioners to better understand the practices and use ML in a more informed way and help researchers to understand possible aspects to consider when studying ML practices</em></strong>.</Typography>
           <br/>
           <Typography variant="caption" align="justify">Authors: Anamaria Mojica-Hanke, Andrea Bayona, Mario Linares-Vásquez, Steffen Herbold, Fabio A. González</Typography>
           <br/>