Developers rely on bug reports to fix bugs. The bug reports are usually stored and managed in bug tracking systems. Due to the different expression habits, different reporters may use different expressions to describe the same bug in the bug tracking system. As a result, the bug tracking system often contains many duplicate bug reports. Automatically detecting these duplicate bug reports would save a large amount of effort for bug analysis. Prior studies have found that deep-learning technique is effective for duplicate bug report detection. Inspired by recent Natural Language Processing (NLP) research, in this paper, we propose a duplicate bug report detection approach based on Dual-Channel Convolutional Neural Networks (DC-CNN). We present a novel bug report pair representation, i.e., dual-channel matrix through concatenating two single-channel matrices representing bug reports. Such bug report pairs are fed to a CNN model to capture the correlated semantic relationships between bug reports. Then, our approach uses the association features to classify whether a pair of bug reports are duplicate or not. We evaluate our approach on three large datasets from three open-source projects, including Open Office, Eclipse, Net Beans and a larger combined dataset, and the accuracy of classification reaches 0.9429, 0.9685, 0.9534, 0.9552 respectively. Such performance outperforms the two state-of-the-art approaches which also use deep-learning techniques. The results indicate that our dual-channel matrix representation is effective for duplicate bug report detection.
{"title":"Duplicate Bug Report Detection Using Dual-Channel Convolutional Neural Networks","authors":"Jianjun He, Ling Xu, Meng Yan, Xin Xia, Yan Lei","doi":"10.1145/3387904.3389263","DOIUrl":"https://doi.org/10.1145/3387904.3389263","url":null,"abstract":"Developers rely on bug reports to fix bugs. The bug reports are usually stored and managed in bug tracking systems. Due to the different expression habits, different reporters may use different expressions to describe the same bug in the bug tracking system. As a result, the bug tracking system often contains many duplicate bug reports. Automatically detecting these duplicate bug reports would save a large amount of effort for bug analysis. Prior studies have found that deep-learning technique is effective for duplicate bug report detection. Inspired by recent Natural Language Processing (NLP) research, in this paper, we propose a duplicate bug report detection approach based on Dual-Channel Convolutional Neural Networks (DC-CNN). We present a novel bug report pair representation, i.e., dual-channel matrix through concatenating two single-channel matrices representing bug reports. Such bug report pairs are fed to a CNN model to capture the correlated semantic relationships between bug reports. Then, our approach uses the association features to classify whether a pair of bug reports are duplicate or not. We evaluate our approach on three large datasets from three open-source projects, including Open Office, Eclipse, Net Beans and a larger combined dataset, and the accuracy of classification reaches 0.9429, 0.9685, 0.9534, 0.9552 respectively. Such performance outperforms the two state-of-the-art approaches which also use deep-learning techniques. The results indicate that our dual-channel matrix representation is effective for duplicate bug report detection.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129528360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Akira Fujimoto, Yoshiki Higo, Junnosuke Matsumoto, S. Kusumoto
In software development, developers often need to understand source code differences in their activities. GumTree is a tool that detects tree-based source code differences. GumTree constructs abstract syntax trees from the source code before and after a given change, and then, it identifies inserted/deleted/moved subtrees and updated nodes. Source code differences are detected based on the four kinds of information in GumTree. However, GumTree calculates the difference for each file individually, so that it cannot detect moves of code fragments across files. In this research, we propose (1) to construct a single abstract syntax tree from all source files included in a project and (2) to perform a staged tree matching to detect across-file code moves efficiently and accurately. We have already conducted a pilot experiment on open source projects with our technique. As a result, we were able to detect code moves across files in all the projects, and the number of such code moves was 76,600 in total.
{"title":"Staged Tree Matching for Detecting Code Move across Files","authors":"Akira Fujimoto, Yoshiki Higo, Junnosuke Matsumoto, S. Kusumoto","doi":"10.1145/3387904.3389289","DOIUrl":"https://doi.org/10.1145/3387904.3389289","url":null,"abstract":"In software development, developers often need to understand source code differences in their activities. GumTree is a tool that detects tree-based source code differences. GumTree constructs abstract syntax trees from the source code before and after a given change, and then, it identifies inserted/deleted/moved subtrees and updated nodes. Source code differences are detected based on the four kinds of information in GumTree. However, GumTree calculates the difference for each file individually, so that it cannot detect moves of code fragments across files. In this research, we propose (1) to construct a single abstract syntax tree from all source files included in a project and (2) to perform a staged tree matching to detect across-file code moves efficiently and accurately. We have already conducted a pilot experiment on open source projects with our technique. As a result, we were able to detect code moves across files in all the projects, and the number of such code moves was 76,600 in total.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134209137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The way how programmers comprehend source code depends on several factors, including the source code itself and the programmer. Recent studies showed that novice programmers tend to read source code more like natural language text, whereas experts tend to follow the program execution flow. But, it is unknown how the linearity of source code and the comprehension strategy influence programmers' linearity of reading order. Objective: We replicate two previous studies with the aim of additionally providing empirical evidence on the influencing effects of linearity of source code and programmers' comprehension strategy on linearity of reading order. Methods: To understand the effects of linearity of source code on reading order, we conducted a non-exact replication of studies by Busjahn et al. and Peachock et al., which compared the reading order of novice and expert programmers. Like the original studies, we used an eye-tracker to record the eye movements of participants (12 novice and 19 intermediate programmers). Results: In line with Busjahn et al. (but different from Peachock et al.), we found that experience modulates the reading behavior of participants. However, the linearity of source code has an even stronger effect on reading order than experience, whereas the comprehension strategy has a minor effect. Implications: Our results demonstrate that studies on the reading behavior of programmers must carefully select source code snippets to control the influence of confounding factors. Furthermore, we identify a need for further studies on how programmers should structure source code to align it with their natural reading behavior to ease program comprehension.
{"title":"What Drives the Reading Order of Programmers? An Eye Tracking Study","authors":"Norman Peitek, J. Siegmund, S. Apel","doi":"10.1145/3387904.3389279","DOIUrl":"https://doi.org/10.1145/3387904.3389279","url":null,"abstract":"Background: The way how programmers comprehend source code depends on several factors, including the source code itself and the programmer. Recent studies showed that novice programmers tend to read source code more like natural language text, whereas experts tend to follow the program execution flow. But, it is unknown how the linearity of source code and the comprehension strategy influence programmers' linearity of reading order. Objective: We replicate two previous studies with the aim of additionally providing empirical evidence on the influencing effects of linearity of source code and programmers' comprehension strategy on linearity of reading order. Methods: To understand the effects of linearity of source code on reading order, we conducted a non-exact replication of studies by Busjahn et al. and Peachock et al., which compared the reading order of novice and expert programmers. Like the original studies, we used an eye-tracker to record the eye movements of participants (12 novice and 19 intermediate programmers). Results: In line with Busjahn et al. (but different from Peachock et al.), we found that experience modulates the reading behavior of participants. However, the linearity of source code has an even stronger effect on reading order than experience, whereas the comprehension strategy has a minor effect. Implications: Our results demonstrate that studies on the reading behavior of programmers must carefully select source code snippets to control the influence of confounding factors. Furthermore, we identify a need for further studies on how programmers should structure source code to align it with their natural reading behavior to ease program comprehension.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132653026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Open Source Software (OSS) projects start with an initial vocabulary, often determined by the first generation of developers. This vocabulary, embedded in code identifier names and internal code comments, goes through multiple rounds of change, influenced by the interrelated patterns of human (e.g., developers joining and departing) and system (e.g., maintenance activities) interactions. Capturing the dynamics of this change is crucial for understanding and synthesizing code changes over time. However, existing code evolution analysis tools, available in modern version control systems such as GitHub and SourceForge, often overlook the linguistic aspects of code evolution. To bridge this gap, in this paper, we propose to study code evolution in OSS projects through the lens of developers' language, also known as code lexicon. Our analysis is conducted using 32 OSS projects sampled from a broad range of application domains. Our results show that different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic program comprehension tasks and help researchers gain new insights into the complex interplay between linguistic change and various system and human aspects of OSS development.
{"title":"Linguistic Documentation of Software History","authors":"Miroslav Tushev, Anas Mahmoud","doi":"10.1145/3387904.3389288","DOIUrl":"https://doi.org/10.1145/3387904.3389288","url":null,"abstract":"Open Source Software (OSS) projects start with an initial vocabulary, often determined by the first generation of developers. This vocabulary, embedded in code identifier names and internal code comments, goes through multiple rounds of change, influenced by the interrelated patterns of human (e.g., developers joining and departing) and system (e.g., maintenance activities) interactions. Capturing the dynamics of this change is crucial for understanding and synthesizing code changes over time. However, existing code evolution analysis tools, available in modern version control systems such as GitHub and SourceForge, often overlook the linguistic aspects of code evolution. To bridge this gap, in this paper, we propose to study code evolution in OSS projects through the lens of developers' language, also known as code lexicon. Our analysis is conducted using 32 OSS projects sampled from a broad range of application domains. Our results show that different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic program comprehension tasks and help researchers gain new insights into the complex interplay between linguistic change and various system and human aspects of OSS development.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Program comprehension is one of the important cognitive processes in software maintenance. The process typically involves diverse mental activities such as understanding of source code, library usages, and requirements. Systematic supports would be improved if the supports can be aware of such fine-grained mental activities during program comprehension. Here we aim to investigate whether biometric data can be varied according to such mental activity classes and conduct an experiment with program comprehension tasks involving multiple documents. As a result, we successfully classified the success/failure of the tasks at 85.2% from electroencephalogram (EEG) combined with focused document types. This result suggests that our metrics based on EEG and focused document types might be beneficial to detect developers, diverse mental activities triggered by different documents.
{"title":"Combining Biometric Data with Focused Document Types Classifies a Success of Program Comprehension","authors":"Toyomi Ishida, H. Uwano, Yoshiharu Ikutani","doi":"10.1145/3387904.3389291","DOIUrl":"https://doi.org/10.1145/3387904.3389291","url":null,"abstract":"Program comprehension is one of the important cognitive processes in software maintenance. The process typically involves diverse mental activities such as understanding of source code, library usages, and requirements. Systematic supports would be improved if the supports can be aware of such fine-grained mental activities during program comprehension. Here we aim to investigate whether biometric data can be varied according to such mental activity classes and conduct an experiment with program comprehension tasks involving multiple documents. As a result, we successfully classified the success/failure of the tasks at 85.2% from electroencephalogram (EEG) combined with focused document types. This result suggests that our metrics based on EEG and focused document types might be beneficial to detect developers, diverse mental activities triggered by different documents.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127306592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emanuele Iannone, Fabiano Pecorelli, D. D. Nucci, Fabio Palomba, A. D. Lucia
Mobile applications are major means to perform daily actions, including social and emergency connectivity. However, their usability is threatened by energy consumption that may be impacted by code smells i.e., symptoms of bad implementation and design practices. In particular, researchers derived a set of mobile-specific code smells resulting in increased energy consumption of mobile apps and removing such smells through refactoring can mitigate the problem. In this paper, we extend and revise ADOCTOR, a tool that we previously implemented to identify energy-related smells. On the one hand, we present and implement automated refactoring solutions to those smells. On the other hand, we make the tool completely open-source and available in Android Studio as a plugin published in the official store. The video showing the tool in action is available at: https://www.youtube.com/watch?v=1c2EhVXiKis
{"title":"Refactoring Android-specific Energy Smells: A Plugin for Android Studio","authors":"Emanuele Iannone, Fabiano Pecorelli, D. D. Nucci, Fabio Palomba, A. D. Lucia","doi":"10.1145/3387904.3389298","DOIUrl":"https://doi.org/10.1145/3387904.3389298","url":null,"abstract":"Mobile applications are major means to perform daily actions, including social and emergency connectivity. However, their usability is threatened by energy consumption that may be impacted by code smells i.e., symptoms of bad implementation and design practices. In particular, researchers derived a set of mobile-specific code smells resulting in increased energy consumption of mobile apps and removing such smells through refactoring can mitigate the problem. In this paper, we extend and revise ADOCTOR, a tool that we previously implemented to identify energy-related smells. On the one hand, we present and implement automated refactoring solutions to those smells. On the other hand, we make the tool completely open-source and available in Android Studio as a plugin published in the official store. The video showing the tool in action is available at: https://www.youtube.com/watch?v=1c2EhVXiKis","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122287490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Markus Raab, B. Denner, Stefan Hahnenberg, Jürgen Cito
The behavior of software is often governed by a large set of configuration settings, distributed over several stacks in the software system. These settings are often manifested as plain text files that exhibit different formats and syntax. Configuration management systems are introduced to manage the complexity of provisioning and distributing configuration in large scale software. Globally patching configuration settings in these systems requires, however, introducing text manipulation or external templating mechanisms, that paradoxically lead to increased complexity and, eventually, to misconfigurations. These issues manifest through crashes or bugs that are often only discovered at runtime. We introduce a framework called Elektra, which integrates a centralized configuration space into configuration management systems to avoid syntax errors and avert the overriding of default values, to increase developer productivity. Elektra enables mounting different configuration files into a common, globally shared data structure to abstract away from the intricate details of file formats and configuration syntax and introduce a unified way to specify and patch configuration settings as key/value pairs. In this work, we integrate Elektra in the configuration management tool Puppet. Additionally, we present a user study with 14 developers showing that Elektra enables significant productivity improvements over existing configuration management concepts. Our study participants performed significantly faster using Elektra in solving three representative scenarios that involve configuration manipulation, compared to other general-purpose configuration manipulation methods.
{"title":"Unified Configuration Setting Access in Configuration Management Systems","authors":"Markus Raab, B. Denner, Stefan Hahnenberg, Jürgen Cito","doi":"10.1145/3387904.3389257","DOIUrl":"https://doi.org/10.1145/3387904.3389257","url":null,"abstract":"The behavior of software is often governed by a large set of configuration settings, distributed over several stacks in the software system. These settings are often manifested as plain text files that exhibit different formats and syntax. Configuration management systems are introduced to manage the complexity of provisioning and distributing configuration in large scale software. Globally patching configuration settings in these systems requires, however, introducing text manipulation or external templating mechanisms, that paradoxically lead to increased complexity and, eventually, to misconfigurations. These issues manifest through crashes or bugs that are often only discovered at runtime. We introduce a framework called Elektra, which integrates a centralized configuration space into configuration management systems to avoid syntax errors and avert the overriding of default values, to increase developer productivity. Elektra enables mounting different configuration files into a common, globally shared data structure to abstract away from the intricate details of file formats and configuration syntax and introduce a unified way to specify and patch configuration settings as key/value pairs. In this work, we integrate Elektra in the configuration management tool Puppet. Additionally, we present a user study with 14 developers showing that Elektra enables significant productivity improvements over existing configuration management concepts. Our study participants performed significantly faster using Elektra in solving three representative scenarios that involve configuration manipulation, compared to other general-purpose configuration manipulation methods.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123894788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sean Stapleton, Yashmeet Gambhir, Alexander LeClair, Zachary Eberhart, Westley Weimer, Kevin Leach, Yu Huang
Software developers spend a great deal of time reading and understanding code that is poorly-documented, written by other developers, or developed using differing styles. During the past decade, researchers have investigated techniques for automatically documenting code to improve comprehensibility. In particular, recent advances in deep learning have led to sophisticated summary generation techniques that convert functions or methods to simple English strings that succinctly describe that code's behavior. However, automatic summarization techniques are assessed using internal metrics such as BLEU scores, which measure natural language properties in translational models, or ROUGE scores, which measure overlap with human-written text. Unfortunately, these metrics do not necessarily capture how machine-generated code summaries actually affect human comprehension or developer productivity. We conducted a human study involving both university students and professional developers (n = 45). Participants reviewed Java methods and summaries and answered established program comprehension questions. In addition, participants completed coding tasks given summaries as specifications. Critically, the experiment controlled the source of the summaries: for a given method, some participants were shown human-written text and some were shown machine-generated text. We found that participants performed significantly better (p = 0.029) using human-written summaries versus machine-generated summaries. However, we found no evidence to support that participants perceive human- and machine-generated summaries to have different qualities. In addition, participants' performance showed no correlation with the BLEU and ROUGE scores often used to assess the quality of machine-generated summaries. These results suggest a need for revised metrics to assess and guide automatic summarization techniques.
{"title":"A Human Study of Comprehension and Code Summarization","authors":"Sean Stapleton, Yashmeet Gambhir, Alexander LeClair, Zachary Eberhart, Westley Weimer, Kevin Leach, Yu Huang","doi":"10.1145/3387904.3389258","DOIUrl":"https://doi.org/10.1145/3387904.3389258","url":null,"abstract":"Software developers spend a great deal of time reading and understanding code that is poorly-documented, written by other developers, or developed using differing styles. During the past decade, researchers have investigated techniques for automatically documenting code to improve comprehensibility. In particular, recent advances in deep learning have led to sophisticated summary generation techniques that convert functions or methods to simple English strings that succinctly describe that code's behavior. However, automatic summarization techniques are assessed using internal metrics such as BLEU scores, which measure natural language properties in translational models, or ROUGE scores, which measure overlap with human-written text. Unfortunately, these metrics do not necessarily capture how machine-generated code summaries actually affect human comprehension or developer productivity. We conducted a human study involving both university students and professional developers (n = 45). Participants reviewed Java methods and summaries and answered established program comprehension questions. In addition, participants completed coding tasks given summaries as specifications. Critically, the experiment controlled the source of the summaries: for a given method, some participants were shown human-written text and some were shown machine-generated text. We found that participants performed significantly better (p = 0.029) using human-written summaries versus machine-generated summaries. However, we found no evidence to support that participants perceive human- and machine-generated summaries to have different qualities. In addition, participants' performance showed no correlation with the BLEU and ROUGE scores often used to assess the quality of machine-generated summaries. These results suggest a need for revised metrics to assess and guide automatic summarization techniques.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124070178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Dias, D. Orellana, S. Vidal, Leonel Merino, Alexandre Bergel
To characterize the building blocks of a legacy software system (e.g., structure, dependencies), programmers usually spend a long time navigating its source code. Yet, modern integrated development environments (IDEs) do not provide appropriate means to efficiently achieve complex software comprehension tasks. To deal with this unfulfilled need, we present Hunter, a tool for the visualization of JavaScript applications. Hunter visualizes source code through a set of coordinated views that include a node-link diagram that depicts the dependencies among the components of a system, and a treemap that helps programmers to orientate when navigating its structure. In this paper, we report on a controlled experiment that evaluates Hunter. We asked 16 participants to solve a set of software comprehension tasks, and assessed their effectiveness in terms of (i) user performance (i.e., completion time, accuracy, and attention), and (ii) user experience (i.e., emotions, usability). We found that when using Hunter programmers required significantly less time to complete various software comprehension tasks and achieved a significantly higher accuracy. We also found that the node-link diagram panel of Hunter gets most of the attention of programmers, whereas the source code panel does so in Visual Studio Code. Moreover, programmers considered that Hunter exhibits a good user experience.
为了描述遗留软件系统的构建块(例如，结构，依赖关系)，程序员通常花费很长时间浏览其源代码。然而，现代集成开发环境(ide)并没有提供适当的方法来有效地实现复杂的软件理解任务。为了解决这个未满足的需求，我们提出了Hunter，一个JavaScript应用程序的可视化工具。Hunter通过一组协调的视图将源代码可视化，这些视图包括一个描述系统组件之间依赖关系的节点链接图，以及一个帮助程序员在导航系统结构时定位的树状图。在本文中，我们报告了一个评估亨特的对照实验。我们要求16名参与者解决一组软件理解任务，并根据(i)用户性能(即，完成时间，准确性和注意力)和(ii)用户体验(即，情感，可用性)评估其有效性。我们发现，当使用Hunter时，程序员需要更少的时间来完成各种软件理解任务，并且获得了更高的准确性。我们还发现，Hunter的节点链接图面板吸引了程序员的大部分注意力，而在Visual Studio code中，源代码面板则是如此。此外，程序员认为Hunter展示了良好的用户体验。
{"title":"Evaluating a Visual Approach for Understanding JavaScript Source Code","authors":"M. Dias, D. Orellana, S. Vidal, Leonel Merino, Alexandre Bergel","doi":"10.1145/3387904.3389275","DOIUrl":"https://doi.org/10.1145/3387904.3389275","url":null,"abstract":"To characterize the building blocks of a legacy software system (e.g., structure, dependencies), programmers usually spend a long time navigating its source code. Yet, modern integrated development environments (IDEs) do not provide appropriate means to efficiently achieve complex software comprehension tasks. To deal with this unfulfilled need, we present Hunter, a tool for the visualization of JavaScript applications. Hunter visualizes source code through a set of coordinated views that include a node-link diagram that depicts the dependencies among the components of a system, and a treemap that helps programmers to orientate when navigating its structure. In this paper, we report on a controlled experiment that evaluates Hunter. We asked 16 participants to solve a set of software comprehension tasks, and assessed their effectiveness in terms of (i) user performance (i.e., completion time, accuracy, and attention), and (ii) user experience (i.e., emotions, usability). We found that when using Hunter programmers required significantly less time to complete various software comprehension tasks and achieved a significantly higher accuracy. We also found that the node-link diagram panel of Hunter gets most of the attention of programmers, whereas the source code panel does so in Visual Studio Code. Moreover, programmers considered that Hunter exhibits a good user experience.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabiano Pecorelli, Gemma Catolino, F. Ferrucci, A. D. Lucia, Fabio Palomba
Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today's world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical studies aiming at assessing the actual quality of test cases developed by mobile developers: this perspective could provide evidence-based findings on the current status of testing in the wild as well as on the future research directions in the field. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.
{"title":"Testing of Mobile Applications in the Wild: A large-Scale Empirical Study on Android Apps","authors":"Fabiano Pecorelli, Gemma Catolino, F. Ferrucci, A. D. Lucia, Fabio Palomba","doi":"10.1145/3387904.3389256","DOIUrl":"https://doi.org/10.1145/3387904.3389256","url":null,"abstract":"Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today's world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical studies aiming at assessing the actual quality of test cases developed by mobile developers: this perspective could provide evidence-based findings on the current status of testing in the wild as well as on the future research directions in the field. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"60 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120935440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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