Ashish Rajendra Sai, Conor Holmes, J. Buckley, AndrewJ. L. Gear
Blockchain systems have gained substantial traction recently, partly due to the potential of decentralized immutable mediation of economic activities. Ethereum is a prominent example that has the provision for executing stateful computing scripts known as Smart Contracts. These smart contracts resemble traditional programs, but with immutability being the core differentiating factor. Given their immutability and potential high monetary value, it becomes imperative to develop high-quality smart contracts. Software metrics have traditionally been an essential tool in determining programming quality. Given the similarity between smart contracts (written in Solidity for Ethereum) and object-oriented (OO) programming, OO metrics would appear applicable. In this paper, we empirically evaluate inheritance-based metrics as applied to smart contracts. We adopt this focus because, traditionally, inheritance has been linked to a more complex codebase which we posit is not the case with Solidity based smart contracts. In this work, we evaluate the hypothesis that, due to the differences in the context of smart contracts and OO programs, it may not be appropriate to use the same interpretation of inheritance based metrics for assessment.
{"title":"Inheritance software metrics on smart contracts","authors":"Ashish Rajendra Sai, Conor Holmes, J. Buckley, AndrewJ. L. Gear","doi":"10.1145/3387904.3389284","DOIUrl":"https://doi.org/10.1145/3387904.3389284","url":null,"abstract":"Blockchain systems have gained substantial traction recently, partly due to the potential of decentralized immutable mediation of economic activities. Ethereum is a prominent example that has the provision for executing stateful computing scripts known as Smart Contracts. These smart contracts resemble traditional programs, but with immutability being the core differentiating factor. Given their immutability and potential high monetary value, it becomes imperative to develop high-quality smart contracts. Software metrics have traditionally been an essential tool in determining programming quality. Given the similarity between smart contracts (written in Solidity for Ethereum) and object-oriented (OO) programming, OO metrics would appear applicable. In this paper, we empirically evaluate inheritance-based metrics as applied to smart contracts. We adopt this focus because, traditionally, inheritance has been linked to a more complex codebase which we posit is not the case with Solidity based smart contracts. In this work, we evaluate the hypothesis that, due to the differences in the context of smart contracts and OO programs, it may not be appropriate to use the same interpretation of inheritance based metrics for assessment.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"20 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":"115793288","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}
Jinglei Zhang, Rui Xie, Wei Ye, Yuhan Zhang, Shikun Zhang
Bug localization automatic localize relevant source files given a natural language description of bug within a software project. For a large project containing hundreds and thousands of source files, developers need cost lots of time to understand bug reports generated by quality assurance and localize these buggy source files. Traditional methods are heavily depending on the information retrieval technologies which rank the similarity between source files and bug reports in lexical level. Recently, deep learning based models are used to extract semantic information of code with significant improvements for bug localization. However, programming language is a highly structural and logical language, which contains various relations within and cross source files. Thus, we propose KGBugLocator to utilize knowledge graph embeddings to extract these interrelations of code, and a keywords supervised bi-directional attention mechanism regularize model with interactive information between source files and bug reports. With extensive experiments on four different projects, we prove our model can reach the new the-state-of-art(SOTA) for bug localization.
{"title":"Exploiting Code Knowledge Graph for Bug Localization via Bi-directional Attention","authors":"Jinglei Zhang, Rui Xie, Wei Ye, Yuhan Zhang, Shikun Zhang","doi":"10.1145/3387904.3389281","DOIUrl":"https://doi.org/10.1145/3387904.3389281","url":null,"abstract":"Bug localization automatic localize relevant source files given a natural language description of bug within a software project. For a large project containing hundreds and thousands of source files, developers need cost lots of time to understand bug reports generated by quality assurance and localize these buggy source files. Traditional methods are heavily depending on the information retrieval technologies which rank the similarity between source files and bug reports in lexical level. Recently, deep learning based models are used to extract semantic information of code with significant improvements for bug localization. However, programming language is a highly structural and logical language, which contains various relations within and cross source files. Thus, we propose KGBugLocator to utilize knowledge graph embeddings to extract these interrelations of code, and a keywords supervised bi-directional attention mechanism regularize model with interactive information between source files and bug reports. With extensive experiments on four different projects, we prove our model can reach the new the-state-of-art(SOTA) for bug localization.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"241 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":"121482832","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}
R. Hebig, Truong Ho-Quang, Rodi Jolak, Jan Schröder, Humberto Linero, Magnus Ågren, Salome Maro
Today, there is a wide range of techniques to support software comprehension. However, we do not fully understand yet what techniques really help novices, to comprehend a software system. In this paper, we present a master level project course on software evolution, which has a large focus on software comprehension. We collected data about student's experience with diverse comprehension techniques during focus group discussions over the course of two years. Our results indicate that systematic code reading can be supported by additional techniques to guiding reading efforts. Most techniques are considered valuable for gaining an overview and some techniques are judged to be helpful only in later stages of software comprehension efforts.
{"title":"How do Students Experience and Judge Software Comprehension Techniques?","authors":"R. Hebig, Truong Ho-Quang, Rodi Jolak, Jan Schröder, Humberto Linero, Magnus Ågren, Salome Maro","doi":"10.1145/3387904.3389283","DOIUrl":"https://doi.org/10.1145/3387904.3389283","url":null,"abstract":"Today, there is a wide range of techniques to support software comprehension. However, we do not fully understand yet what techniques really help novices, to comprehend a software system. In this paper, we present a master level project course on software evolution, which has a large focus on software comprehension. We collected data about student's experience with diverse comprehension techniques during focus group discussions over the course of two years. Our results indicate that systematic code reading can be supported by additional techniques to guiding reading efforts. Most techniques are considered valuable for gaining an overview and some techniques are judged to be helpful only in later stages of software comprehension efforts.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"57 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":"124016354","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}
Comments are the internal documentation of corresponding code blocks, which are essential to understand and maintain a software. In large scale software development, developers need to analyze existing codes, where comments assist better readability. In practice, developers commonly ignore comments' updating with respect to changing codes, which leads the code comment inconsistency. Traditionally researchers detect these inconsistencies based on code-comment tokens. However, sequence ordering in codecomments is ignored in existing solution, as a result inconsistencies for invalid sequences of codes and comments are neglected. This paper solves these inconsistencies using siamese recurrent network which uses word tokens in codes and comments as well as their sequences in corresponding codes or comments. Proposed approach has been evaluated with a benchmark dataset, along with the ability of detecting invalid code comment sequence is examined.
{"title":"Detecting Code Comment Inconsistency using Siamese Recurrent Network","authors":"F. Rabbi, Md. Saeed Siddik","doi":"10.1145/3387904.3389286","DOIUrl":"https://doi.org/10.1145/3387904.3389286","url":null,"abstract":"Comments are the internal documentation of corresponding code blocks, which are essential to understand and maintain a software. In large scale software development, developers need to analyze existing codes, where comments assist better readability. In practice, developers commonly ignore comments' updating with respect to changing codes, which leads the code comment inconsistency. Traditionally researchers detect these inconsistencies based on code-comment tokens. However, sequence ordering in codecomments is ignored in existing solution, as a result inconsistencies for invalid sequences of codes and comments are neglected. This paper solves these inconsistencies using siamese recurrent network which uses word tokens in codes and comments as well as their sequences in corresponding codes or comments. Proposed approach has been evaluated with a benchmark dataset, along with the ability of detecting invalid code comment sequence is examined.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"22 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":"126860989","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}
Devjeet Roy, Sarah Fakhoury, John Lee, V. Arnaoudova
Identifying source code that has poor readability allows developers to focus maintenance efforts on problematic code. Therefore, the effort to develop models that can quantify the readability of a piece of source code has been an area of interest for software engineering researchers for several years. However, recent research questions the usefulness of these readability models in practice. When applying these models to readability improvements that are made in practice, i.e., commits, they are unable to capture these incremental improvements, despite a clear perceived improvement by the developers. This results in a discrepancy between the models we have built to measure readability, and the actual perception of readability in practice. In this work, we propose a model that is able to detect incremental readability improvements made by developers in practice with an average precision of 79.2% and an average recall of 67% on an unseen test set. We then investigate the metrics that our model associates with developer perceived readability improvements as well as non-readability changes. Finally, we compare our model to existing state-of-the-art readability models, which our model outperforms by at least 23% in terms of precision and 42% in terms of recall.
{"title":"A Model to Detect Readability Improvements in Incremental Changes","authors":"Devjeet Roy, Sarah Fakhoury, John Lee, V. Arnaoudova","doi":"10.1145/3387904.3389255","DOIUrl":"https://doi.org/10.1145/3387904.3389255","url":null,"abstract":"Identifying source code that has poor readability allows developers to focus maintenance efforts on problematic code. Therefore, the effort to develop models that can quantify the readability of a piece of source code has been an area of interest for software engineering researchers for several years. However, recent research questions the usefulness of these readability models in practice. When applying these models to readability improvements that are made in practice, i.e., commits, they are unable to capture these incremental improvements, despite a clear perceived improvement by the developers. This results in a discrepancy between the models we have built to measure readability, and the actual perception of readability in practice. In this work, we propose a model that is able to detect incremental readability improvements made by developers in practice with an average precision of 79.2% and an average recall of 67% on an unseen test set. We then investigate the metrics that our model associates with developer perceived readability improvements as well as non-readability changes. Finally, we compare our model to existing state-of-the-art readability models, which our model outperforms by at least 23% in terms of precision and 42% in terms of recall.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"96 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":"129897041","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}
Ana Carla Bibiano, Vinícius Soares, Daniel Coutinho, Eduardo Fernandes, J. Correia, Kleber Santos, Anderson Oliveira, Alessandro F. Garcia, Rohit Gheyi, B. Neto, Márcio Ribeiro, Caio Barbosa, Daniel Oliveira
Program refactoring consists of code changes applied to improve the internal structure of a program and, as a consequence, its comprehensibility. Recent studies indicate that developers often perform composite refactorings, i.e., a set of two or more interrelated single refactorings. Recent studies also recommend certain patterns of composite refactorings to fully remove poor code structures, i.e, code smells, thus further improving the program comprehension. However, other recent studies report that composite refactorings often fail to fully remove code smells. Given their failure to achieve this purpose, these composite refactorings are considered incomplete, i.e, they are not able to entirely remove a smelly structure. Unfortunately, there is no study providing an in-depth analysis of the incompleteness nature of many composites and their possibly partial impact on improving, maybe decreasing, internal quality attributes. This paper identifies the most common forms of incomplete composites, and their effect on quality attributes, such as coupling and cohesion, which are known to have an impact on program comprehension. We analyzed 353 incomplete composite refactorings in 5 software projects, two common code smells (Feature Envy and God Class), and four internal quality attributes. Our results reveal that incomplete composite refactorings with at least one Extract Method are often (71%) applied without Move Methods on smelly classes. We have also found that most incomplete composite refactorings (58%) tended to at least maintain the internal structural quality of smelly classes, thereby not causing more harm to program comprehension. We also discuss the implications of our findings to the research and practice of composite refactoring.
{"title":"How Does Incomplete Composite Refactoring Affect Internal Quality Attributes?","authors":"Ana Carla Bibiano, Vinícius Soares, Daniel Coutinho, Eduardo Fernandes, J. Correia, Kleber Santos, Anderson Oliveira, Alessandro F. Garcia, Rohit Gheyi, B. Neto, Márcio Ribeiro, Caio Barbosa, Daniel Oliveira","doi":"10.1145/3387904.3389264","DOIUrl":"https://doi.org/10.1145/3387904.3389264","url":null,"abstract":"Program refactoring consists of code changes applied to improve the internal structure of a program and, as a consequence, its comprehensibility. Recent studies indicate that developers often perform composite refactorings, i.e., a set of two or more interrelated single refactorings. Recent studies also recommend certain patterns of composite refactorings to fully remove poor code structures, i.e, code smells, thus further improving the program comprehension. However, other recent studies report that composite refactorings often fail to fully remove code smells. Given their failure to achieve this purpose, these composite refactorings are considered incomplete, i.e, they are not able to entirely remove a smelly structure. Unfortunately, there is no study providing an in-depth analysis of the incompleteness nature of many composites and their possibly partial impact on improving, maybe decreasing, internal quality attributes. This paper identifies the most common forms of incomplete composites, and their effect on quality attributes, such as coupling and cohesion, which are known to have an impact on program comprehension. We analyzed 353 incomplete composite refactorings in 5 software projects, two common code smells (Feature Envy and God Class), and four internal quality attributes. Our results reveal that incomplete composite refactorings with at least one Extract Method are often (71%) applied without Move Methods on smelly classes. We have also found that most incomplete composite refactorings (58%) tended to at least maintain the internal structural quality of smelly classes, thereby not causing more harm to program comprehension. We also discuss the implications of our findings to the research and practice of composite refactoring.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"39 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":"115165133","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}
Amal A. Al-Shargabi, S. A. Aljunid, Muthukkaruppan Annamalai, A. Zin
Program comprehension is challenging for many novice developers. Literature indicates that program comprehension is greatly influenced by the specific purpose of reading a program, i.e., the task. However, the task has often been used in research as a measure for program comprehension. Our study takes an inverse approach to investigate the effect of using the task as a facilitator to improve novice developers program comprehension. To measure the effect, our previously published program comprehension mental model of novice developers was utilized. In a sense, the study provides an empirical evaluation of our proposed model in terms of its ability to capture the novice developer's mental model properly. The comprehensive experiment involved one hundred and seventy-eight (178) novice developers from three (3) universities and investigated the effect of six (6) tasks with difficulties ranked according to the cognitive categories of Revised Bloom Taxonomy. The results of the experiment confirmed that performing the tasks can improve program comprehension of novice developers. It demonstrated that different tasks improved different abstraction levels of the mental model and further indicated that higher cognitive category tasks improve program comprehension mental model at higher abstraction levels. The results also showed that the mental model we have proposed earlier is able to capture what novice developers know in response to the tasks they perform. The general implication of the study is that the tasks can be an effective tool for computing educators to incorporate program comprehension in programming courses, whereby these tasks need to be introduced in stages in the teaching of programming; starting initially from the lower cognitive categories' tasks such as Recall and culminating at the higher cognitive categories' tasks such as Modification by first taking into consideration the novices' programming levels.
{"title":"Performing Tasks Can Improve Program Comprehension Mental Model of Novice Developers: An Empirical Approach","authors":"Amal A. Al-Shargabi, S. A. Aljunid, Muthukkaruppan Annamalai, A. Zin","doi":"10.1145/3387904.3389277","DOIUrl":"https://doi.org/10.1145/3387904.3389277","url":null,"abstract":"Program comprehension is challenging for many novice developers. Literature indicates that program comprehension is greatly influenced by the specific purpose of reading a program, i.e., the task. However, the task has often been used in research as a measure for program comprehension. Our study takes an inverse approach to investigate the effect of using the task as a facilitator to improve novice developers program comprehension. To measure the effect, our previously published program comprehension mental model of novice developers was utilized. In a sense, the study provides an empirical evaluation of our proposed model in terms of its ability to capture the novice developer's mental model properly. The comprehensive experiment involved one hundred and seventy-eight (178) novice developers from three (3) universities and investigated the effect of six (6) tasks with difficulties ranked according to the cognitive categories of Revised Bloom Taxonomy. The results of the experiment confirmed that performing the tasks can improve program comprehension of novice developers. It demonstrated that different tasks improved different abstraction levels of the mental model and further indicated that higher cognitive category tasks improve program comprehension mental model at higher abstraction levels. The results also showed that the mental model we have proposed earlier is able to capture what novice developers know in response to the tasks they perform. The general implication of the study is that the tasks can be an effective tool for computing educators to incorporate program comprehension in programming courses, whereby these tasks need to be introduced in stages in the teaching of programming; starting initially from the lower cognitive categories' tasks such as Recall and culminating at the higher cognitive categories' tasks such as Modification by first taking into consideration the novices' programming levels.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"33 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":"123919601","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}
Blocking bugs are a severe type of bugs that prevent other bugs from being fixed. As software becomes increasingly complex and large, blocking bugs occur in many large-scale software, especially in software ecosystems. Blocking bugs may have a high negative impact on software development and maintenance. Usually, blocking bugs preventing more bugs should be more concerned. In this paper, we focus on a special type of blocking bugs that block at least two bugs, which we call Critical Blocking Bugs (CBBs). We study CBBs from the following five aspects: the importance, the repair time, the scale of repair, the experience of developers who repair CBBs, and the circumstance why CBBs block multiple bugs. We build a dataset containing five open source projects and classify bugs into three types (i.e., critical blocking bugs, normal blocking bugs and other bugs, which block at least two, just one and zero bugs, respectively) to compare the differences between CBBs and other types of bugs. The experimental results show that CBBs are more important with longer repair time and larger repair scale, and CBBs are concentrated on parts of components of the project. These results highlight that CBBs are different from other types of bugs in many aspects, and we should pay more attention to such bugs in the future software maintenance process.
{"title":"An Empirical Study on Critical Blocking Bugs","authors":"Hao Ren, Yanhui Li, Lin Chen","doi":"10.1145/3387904.3389267","DOIUrl":"https://doi.org/10.1145/3387904.3389267","url":null,"abstract":"Blocking bugs are a severe type of bugs that prevent other bugs from being fixed. As software becomes increasingly complex and large, blocking bugs occur in many large-scale software, especially in software ecosystems. Blocking bugs may have a high negative impact on software development and maintenance. Usually, blocking bugs preventing more bugs should be more concerned. In this paper, we focus on a special type of blocking bugs that block at least two bugs, which we call Critical Blocking Bugs (CBBs). We study CBBs from the following five aspects: the importance, the repair time, the scale of repair, the experience of developers who repair CBBs, and the circumstance why CBBs block multiple bugs. We build a dataset containing five open source projects and classify bugs into three types (i.e., critical blocking bugs, normal blocking bugs and other bugs, which block at least two, just one and zero bugs, respectively) to compare the differences between CBBs and other types of bugs. The experimental results show that CBBs are more important with longer repair time and larger repair scale, and CBBs are concentrated on parts of components of the project. These results highlight that CBBs are different from other types of bugs in many aspects, and we should pay more attention to such bugs in the future software maintenance process.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"102 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":"124337573","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}
Mohammad D. Alahmadi, Abdulkarim Khormi, S. Haiduc
Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs. We propose a novel approach, called UIScreens, which detects, extracts, and presents the most representative user interface (UI) screens embedded in mobile development screencasts. This could help developers quickly comprehend what an app displayed in a video is about, therefore saving time searching for useful videos. UIScreens has been evaluated in two empirical studies on iOS and Android programming screencasts. The first study investigates the accuracy of our UI extraction and shows that our approach is able to detect and extract UI screens with an accuracy of 94%. The second is a user study with mobile app developers, who evaluated both the accuracy and the usefulness of UI Screens. They agreed that UIScreens is accurate and extracts representative UI screens from videos. They considered that the extracted UI screens are useful for understanding what a video is about and if it is relevant to a search. Our approach has been implemented as a free online tool.
{"title":"UI Screens Identification and Extraction from Mobile Programming Screencasts","authors":"Mohammad D. Alahmadi, Abdulkarim Khormi, S. Haiduc","doi":"10.1145/3387904.3389265","DOIUrl":"https://doi.org/10.1145/3387904.3389265","url":null,"abstract":"Mobile applications demand is on the rise, leading to more programmers learning to develop or having to maintain this kind of programs. Developers often refer to online resources to find inspiration or answers to questions they have about mobile programming topics and screencasts are a popular resource. However, given the multitude of screencasts available, it can be difficult to quickly comprehend which of the many videos is relevant to one's needs. We propose a novel approach, called UIScreens, which detects, extracts, and presents the most representative user interface (UI) screens embedded in mobile development screencasts. This could help developers quickly comprehend what an app displayed in a video is about, therefore saving time searching for useful videos. UIScreens has been evaluated in two empirical studies on iOS and Android programming screencasts. The first study investigates the accuracy of our UI extraction and shows that our approach is able to detect and extract UI screens with an accuracy of 94%. The second is a user study with mobile app developers, who evaluated both the accuracy and the usefulness of UI Screens. They agreed that UIScreens is accurate and extracts representative UI screens from videos. They considered that the extracted UI screens are useful for understanding what a video is about and if it is relevant to a search. Our approach has been implemented as a free online tool.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"18 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":"121144084","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}
Software traceability provides support for various engineering activities including Program Comprehension; however, it can be challenging and arduous to complete in large industrial projects. Researchers have proposed automated traceability techniques to create, maintain and leverage trace links. Computationally intensive techniques, such as repository mining and deep learning, have showed the capability to deliver accurate trace links. The objective of achieving trusted, automated tracing techniques at industrial scale has not yet been successfully accomplished due to practical performance challenges. This paper evaluates high-performance solutions for deploying effective, computationally expensive traceability algorithms in large scale industrial projects and leverages generated trace links to answer Program Comprehension Queries. We comparatively evaluate four different platforms for supporting industrial-scale tracing solutions, capable of tackling software projects with millions of artifacts. We demonstrate that tracing solutions built using big data frameworks scale well for large projects and that our Spark implementation outperforms relational database, graph database (GraphDB), and plain Java implementations. These findings contradict earlier results which suggested that GraphDB solutions should be adopted for large-scale tracing problems.
{"title":"Supporting Program Comprehension through Fast Query response in Large-Scale Systems","authors":"Jinfeng Lin, Yalin Liu, J. Cleland-Huang","doi":"10.1145/3387904.3389260","DOIUrl":"https://doi.org/10.1145/3387904.3389260","url":null,"abstract":"Software traceability provides support for various engineering activities including Program Comprehension; however, it can be challenging and arduous to complete in large industrial projects. Researchers have proposed automated traceability techniques to create, maintain and leverage trace links. Computationally intensive techniques, such as repository mining and deep learning, have showed the capability to deliver accurate trace links. The objective of achieving trusted, automated tracing techniques at industrial scale has not yet been successfully accomplished due to practical performance challenges. This paper evaluates high-performance solutions for deploying effective, computationally expensive traceability algorithms in large scale industrial projects and leverages generated trace links to answer Program Comprehension Queries. We comparatively evaluate four different platforms for supporting industrial-scale tracing solutions, capable of tackling software projects with millions of artifacts. We demonstrate that tracing solutions built using big data frameworks scale well for large projects and that our Spark implementation outperforms relational database, graph database (GraphDB), and plain Java implementations. These findings contradict earlier results which suggested that GraphDB solutions should be adopted for large-scale tracing problems.","PeriodicalId":231095,"journal":{"name":"2020 IEEE/ACM 28th International Conference on Program Comprehension (ICPC)","volume":"18 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":"131117623","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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