Introduction to the Special Issue on Program Comprehension

Introduction to the Special Issue on Program Comprehension. Hayashi, S., Gu�h�neuc, Y., & Chaudron, M. R. V. In Hayashi, S., Gu�h�neuc, Y., & Chaudron, M. R. V., editors, Empirical Software Engineering, volume 28, 1, pages 68. Springer, February, 2023. 1 pages.

Paper abstract bibtex

The Research Track of the 28th IEEE/ACM International Conference on Program Comprehension (ICPC 2020) provided a quality forum for researchers and practitioners from academia, industry, and government to present and discuss new results, negative results, and replications in program-comprehension research. The Research Track welcomed submissions on any program comprehension research and accepted 32 papers out of 84 submissions. For this special issue, we invited authors of five high-quality papers presented in the Research Track of ICPC 2020 to submit an extension of their papers. The program co-chairs selected the top five papers with the highest ratings from the reviewers. Each of these papers received all positive ratings. Eventually, four extensions were submitted by their authors. Three experts reviewed each submission to guarantee the quality and sufficient novelty of the extensions. Some reviewers of the extensions had reviewed the original ICPC papers. Finally, we accepted the following three papers: \beginitemizeıtem The paper entitled Quick remedy commits and their impact on mining software repositories, authored by Fengcai Wen, Csaba Nagy, Michele Lanza, and Gabriele Bavota, presents a study investigating ``quick remedy commits'' performed by developers to implement changes omitted in previous commits. Through a manual analysis of 500 quick remedy commits, the authors defined a taxonomy of the types of changes that developers tend to omit. The authors showed that consideration of quick remedy commits accounts for some noisy data points when performing commit mining. ıtem The paper entitled Software testing and Android applications: a large-scale empirical study, authored by Fabiano Pecorelli, Gemma Catolino, Filomena Ferrucci, Andrea De Lucia, and Fabio Palomba, presents a large-scale empirical study targeting 1,693 open-source Android apps and assessing the extent to which these apps are actually tested, how well-designed are their tests, etc. The authors showed that Android apps are poorly tested and that their tests have low design quality, effectiveness, and ability to find defects. ıtem The paper entitled A unified multi-task learning model for AST-level and token-level code completion, authored by Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, and Zhi Jin, proposes an approach to code completion based on neural networks. It overcomes the limitations of previous approaches by combining AST-level and token-level code completion. Hence, it can take into account token probability but also syntactic structure and semantic relationships. The authors showed that this novel approach is more effective than previous ones through experiments. \enditemize We would like to thank the authors of these papers. We also thank the reviewers who helped authors improve their papers. Finally, we would like to thank the editorial board of the Springer Empirical Software Engineering Journal, who provided the opportunity for this special issue and greatly assisted the editing process.

@INCOLLECTION{Hayashi23-EMSEGuestIntroduction,
   AUTHOR       = {Shinpei Hayashi and Yann-Ga�l Gu�h�neuc and 
      Michel R. V. Chaudron},
   BOOKTITLE    = {Empirical Software Engineering},
   PUBLISHER    = {Springer},
   TITLE        = {Introduction to the Special Issue on Program 
      Comprehension},
   YEAR         = {2023},
   OPTADDRESS   = {},
   CHAPTER      = {1},
   OPTCROSSREF  = {},
   OPTEDITION   = {},
   EDITOR       = {Shinpei Hayashi and Yann-Ga�l Gu�h�neuc and 
      Michel R. V. Chaudron},
   MONTH        = {February},
   NOTE         = {1 pages.},
   OPTNUMBER    = {},
   PAGES        = {68},
   OPTSERIES    = {},
   OPTTYPE      = {},
   VOLUME       = {28},
   KEYWORDS     = {Topic: <b>Program comprehension</b>, Venue: <b>EMSE</b>},
   URL          = {http://www.ptidej.net/publications/documents/EMSE23.doc.pdf},
   ABSTRACT     = {The Research Track of the 28th IEEE/ACM International 
      Conference on Program Comprehension (ICPC 2020) provided a quality 
      forum for researchers and practitioners from academia, industry, and 
      government to present and discuss new results, negative results, and 
      replications in program-comprehension research. The Research Track 
      welcomed submissions on any program comprehension research and 
      accepted 32 papers out of 84 submissions. For this special issue, we 
      invited authors of five high-quality papers presented in the Research 
      Track of ICPC 2020 to submit an extension of their papers. The 
      program co-chairs selected the top five papers with the highest 
      ratings from the reviewers. Each of these papers received all 
      positive ratings. Eventually, four extensions were submitted by their 
      authors. Three experts reviewed each submission to guarantee the 
      quality and sufficient novelty of the extensions. Some reviewers of 
      the extensions had reviewed the original ICPC papers. Finally, we 
      accepted the following three papers: \begin{itemize}\item The paper 
      entitled Quick remedy commits and their impact on mining software 
      repositories, authored by Fengcai Wen, Csaba Nagy, Michele Lanza, and 
      Gabriele Bavota, presents a study investigating ``quick remedy 
      commits'' performed by developers to implement changes omitted in 
      previous commits. Through a manual analysis of 500 quick remedy 
      commits, the authors defined a taxonomy of the types of changes that 
      developers tend to omit. The authors showed that consideration of 
      quick remedy commits accounts for some noisy data points when 
      performing commit mining. \item The paper entitled Software testing 
      and Android applications: a large-scale empirical study, authored by 
      Fabiano Pecorelli, Gemma Catolino, Filomena Ferrucci, Andrea De 
      Lucia, and Fabio Palomba, presents a large-scale empirical study 
      targeting 1,693 open-source Android apps and assessing the extent to 
      which these apps are actually tested, how well-designed are their 
      tests, etc. The authors showed that Android apps are poorly tested 
      and that their tests have low design quality, effectiveness, and 
      ability to find defects. \item The paper entitled A unified 
      multi-task learning model for AST-level and token-level code 
      completion, authored by Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi 
      Fu, and Zhi Jin, proposes an approach to code completion based on 
      neural networks. It overcomes the limitations of previous approaches 
      by combining AST-level and token-level code completion. Hence, it can 
      take into account token probability but also syntactic structure and 
      semantic relationships. The authors showed that this novel approach 
      is more effective than previous ones through experiments. 
      \end{itemize} We would like to thank the authors of these papers. We 
      also thank the reviewers who helped authors improve their papers. 
      Finally, we would like to thank the editorial board of the Springer 
      Empirical Software Engineering Journal, who provided the opportunity 
      for this special issue and greatly assisted the editing process.}
}

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The Research Track welcomed submissions on any program comprehension research and accepted 32 papers out of 84 submissions. For this special issue, we invited authors of five high-quality papers presented in the Research Track of ICPC 2020 to submit an extension of their papers. The program co-chairs selected the top five papers with the highest ratings from the reviewers. Each of these papers received all positive ratings. Eventually, four extensions were submitted by their authors. Three experts reviewed each submission to guarantee the quality and sufficient novelty of the extensions. Some reviewers of the extensions had reviewed the original ICPC papers. Finally, we accepted the following three papers: \\beginitemizeıtem The paper entitled Quick remedy commits and their impact on mining software repositories, authored by Fengcai Wen, Csaba Nagy, Michele Lanza, and Gabriele Bavota, presents a study investigating ``quick remedy commits'' performed by developers to implement changes omitted in previous commits. Through a manual analysis of 500 quick remedy commits, the authors defined a taxonomy of the types of changes that developers tend to omit. The authors showed that consideration of quick remedy commits accounts for some noisy data points when performing commit mining. ıtem The paper entitled Software testing and Android applications: a large-scale empirical study, authored by Fabiano Pecorelli, Gemma Catolino, Filomena Ferrucci, Andrea De Lucia, and Fabio Palomba, presents a large-scale empirical study targeting 1,693 open-source Android apps and assessing the extent to which these apps are actually tested, how well-designed are their tests, etc. The authors showed that Android apps are poorly tested and that their tests have low design quality, effectiveness, and ability to find defects. ıtem The paper entitled A unified multi-task learning model for AST-level and token-level code completion, authored by Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, and Zhi Jin, proposes an approach to code completion based on neural networks. It overcomes the limitations of previous approaches by combining AST-level and token-level code completion. Hence, it can take into account token probability but also syntactic structure and semantic relationships. The authors showed that this novel approach is more effective than previous ones through experiments. \\enditemize We would like to thank the authors of these papers. We also thank the reviewers who helped authors improve their papers. Finally, we would like to thank the editorial board of the Springer Empirical Software Engineering Journal, who provided the opportunity for this special issue and greatly assisted the editing process.","bibtex":"@INCOLLECTION{Hayashi23-EMSEGuestIntroduction,\r\n AUTHOR = {Shinpei Hayashi and Yann-Ga�l Gu�h�neuc and \r\n Michel R. V. 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The authors showed that consideration of \r\n quick remedy commits accounts for some noisy data points when \r\n performing commit mining. \\item The paper entitled Software testing \r\n and Android applications: a large-scale empirical study, authored by \r\n Fabiano Pecorelli, Gemma Catolino, Filomena Ferrucci, Andrea De \r\n Lucia, and Fabio Palomba, presents a large-scale empirical study \r\n targeting 1,693 open-source Android apps and assessing the extent to \r\n which these apps are actually tested, how well-designed are their \r\n tests, etc. The authors showed that Android apps are poorly tested \r\n and that their tests have low design quality, effectiveness, and \r\n ability to find defects. \\item The paper entitled A unified \r\n multi-task learning model for AST-level and token-level code \r\n completion, authored by Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi \r\n Fu, and Zhi Jin, proposes an approach to code completion based on \r\n neural networks. 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