Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00063
Michael D. Shah
When software does not meet performance requirements, difficult decisions are made to change central data structures which may be costly financially and increase development time. In addition, monitoring how these data structures are used, and trying to understand performance implications of any change may prevent any evolution of the original infrastructure. Thus, radical revisions to software may be avoided due to the barriers of time and engineering complexity costs. Our solution to helping developers make infrastructure changes to improve performance is to provide a refactoring tool where developers may swap data structures. Our tool preserves correctness by utilizing the software's test suite and also measures performance automatically of the swapped data structure. We believe there is need for such a tool to help encourage more radical revisions and experimentation in large software projects to improve performance. Our frameworks success will be evaluated based on preserving the correctness of the software within a developer created test suite while providing performance information based on modified data structures.
{"title":"Lib Metamorphosis: A Performance Analysis Framework for Exchanging Data Structures in Performance Sensitive Applications","authors":"Michael D. Shah","doi":"10.1109/ICSME.2019.00063","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00063","url":null,"abstract":"When software does not meet performance requirements, difficult decisions are made to change central data structures which may be costly financially and increase development time. In addition, monitoring how these data structures are used, and trying to understand performance implications of any change may prevent any evolution of the original infrastructure. Thus, radical revisions to software may be avoided due to the barriers of time and engineering complexity costs. Our solution to helping developers make infrastructure changes to improve performance is to provide a refactoring tool where developers may swap data structures. Our tool preserves correctness by utilizing the software's test suite and also measures performance automatically of the swapped data structure. We believe there is need for such a tool to help encourage more radical revisions and experimentation in large software projects to improve performance. Our frameworks success will be evaluated based on preserving the correctness of the software within a developer created test suite while providing performance information based on modified data structures.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127223374","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}
Pub Date : 2019-08-22DOI: 10.1109/ICSME.2019.00067
Zhipeng Gao, Vinoj Jayasundara, Lingxiao Jiang, Xin Xia, D. Lo, J. Grundy
Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, a major concern in Ethereum is the security of its smart contracts. Many identified bugs and vulnerabilities in smart contracts not only present challenges to the maintenance of blockchain, but also lead to serious financial loses. There is a significant need to better assist developers in checking smart contracts and ensuring their reliability. In this paper, we propose a web service tool, named SmartEmbed, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts. Our tool is based on code embeddings and similarity checking techniques. By comparing the similarities among the code embedding vectors for existing solidity code in the Ethereum blockchain and known bugs, we are able to efficiently identify code clones and clone-related bugs for any solidity code given by users, which can help to improve the users' confidence in the reliability of their code. In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale. When applied to more than 22K solidity contracts collected from the Ethereum blockchain, we found that the clone ratio of solidity code is close to 90%, much higher than traditional software, and 194 clone-related bugs can be identified efficiently and accurately based on our small bug database with a precision of 96%.
{"title":"SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding","authors":"Zhipeng Gao, Vinoj Jayasundara, Lingxiao Jiang, Xin Xia, D. Lo, J. Grundy","doi":"10.1109/ICSME.2019.00067","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00067","url":null,"abstract":"Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, a major concern in Ethereum is the security of its smart contracts. Many identified bugs and vulnerabilities in smart contracts not only present challenges to the maintenance of blockchain, but also lead to serious financial loses. There is a significant need to better assist developers in checking smart contracts and ensuring their reliability. In this paper, we propose a web service tool, named SmartEmbed, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts. Our tool is based on code embeddings and similarity checking techniques. By comparing the similarities among the code embedding vectors for existing solidity code in the Ethereum blockchain and known bugs, we are able to efficiently identify code clones and clone-related bugs for any solidity code given by users, which can help to improve the users' confidence in the reliability of their code. In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale. When applied to more than 22K solidity contracts collected from the Ethereum blockchain, we found that the clone ratio of solidity code is close to 90%, much higher than traditional software, and 194 clone-related bugs can be identified efficiently and accurately based on our small bug database with a precision of 96%.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114545937","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}
Pub Date : 2019-08-22DOI: 10.1109/ICSME.2019.00013
Luis Cruz, R. Abreu, J. Grundy, Li Li, Xin Xia
Energy efficiency is a crucial quality requirement for mobile applications. However, improving energy efficiency is far from trivial as developers lack the knowledge and tools to aid in this activity. In this paper we study the impact of changes to improve energy efficiency on the maintainability of Android applications. Using a dataset containing 539 energy efficiency-oriented commits, we measure maintainability – as computed by the Software Improvement Group's web-based source code analysis service Better Code Hub (BCH) – before and after energy efficiency-related code changes. Results show that in general improving energy efficiency comes with a significant decrease in maintainability. This is particularly evident in code changes to accommodate the Power Save Mode and Wakelock Addition energy patterns. In addition, we perform manual analysis to assess how real examples of energy-oriented changes affect maintainability. Our results help mobile app developers to 1) avoid common maintainability issues when improving the energy efficiency of their apps; and 2) adopt development processes to build maintainable and energy-efficient code. We also support researchers by identifying challenges in mobile app development that still need to be addressed.
{"title":"Do Energy-Oriented Changes Hinder Maintainability?","authors":"Luis Cruz, R. Abreu, J. Grundy, Li Li, Xin Xia","doi":"10.1109/ICSME.2019.00013","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00013","url":null,"abstract":"Energy efficiency is a crucial quality requirement for mobile applications. However, improving energy efficiency is far from trivial as developers lack the knowledge and tools to aid in this activity. In this paper we study the impact of changes to improve energy efficiency on the maintainability of Android applications. Using a dataset containing 539 energy efficiency-oriented commits, we measure maintainability – as computed by the Software Improvement Group's web-based source code analysis service Better Code Hub (BCH) – before and after energy efficiency-related code changes. Results show that in general improving energy efficiency comes with a significant decrease in maintainability. This is particularly evident in code changes to accommodate the Power Save Mode and Wakelock Addition energy patterns. In addition, we perform manual analysis to assess how real examples of energy-oriented changes affect maintainability. Our results help mobile app developers to 1) avoid common maintainability issues when improving the energy efficiency of their apps; and 2) adopt development processes to build maintainable and energy-efficient code. We also support researchers by identifying challenges in mobile app development that still need to be addressed.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":" 20","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133019836","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}
Pub Date : 2019-08-06DOI: 10.1109/ICSME.2019.00043
Omar Elazhary, M. Storey, Neil A. Ernst, A. Zaidman
Developer contribution guidelines are used in social coding sites like GitHub to explain and shape the process a project expects contributors to follow. They set standards for all participants and "save time and hassle caused by improperly created pull requests or issues that have to be rejected and re-submitted" (GitHub). Yet, we lack a systematic understanding of the content of a typical contribution guideline, as well as the extent to which these guidelines are followed in practice. Additionally, understanding how guidelines may impact projects that use Continuous Integration as part of the contribution process is of particular interest. To address this knowledge gap, we conducted a mixed-methods study of 53 GitHub projects with explicit contribution guidelines and coded the guidelines to extract key themes. We then created a process model using GitHub activity data (e.g., commit, new issue, new pull request) to compare the actual activity with the prescribed contribution guidelines. We show that approximately 68% of these projects diverge significantly from the expected process.
{"title":"Do as I Do, Not as I Say: Do Contribution Guidelines Match the GitHub Contribution Process?","authors":"Omar Elazhary, M. Storey, Neil A. Ernst, A. Zaidman","doi":"10.1109/ICSME.2019.00043","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00043","url":null,"abstract":"Developer contribution guidelines are used in social coding sites like GitHub to explain and shape the process a project expects contributors to follow. They set standards for all participants and \"save time and hassle caused by improperly created pull requests or issues that have to be rejected and re-submitted\" (GitHub). Yet, we lack a systematic understanding of the content of a typical contribution guideline, as well as the extent to which these guidelines are followed in practice. Additionally, understanding how guidelines may impact projects that use Continuous Integration as part of the contribution process is of particular interest. To address this knowledge gap, we conducted a mixed-methods study of 53 GitHub projects with explicit contribution guidelines and coded the guidelines to extract key themes. We then created a process model using GitHub activity data (e.g., commit, new issue, new pull request) to compare the actual activity with the prescribed contribution guidelines. We show that approximately 68% of these projects diverge significantly from the expected process.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133066784","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}
Pub Date : 2019-08-01DOI: 10.1109/ICSME.2019.00096
R. R. Almeida
Technical debt happens when teams take shortcuts on software development to gain short-term benefits at the cost of making future changes more expensive. Previous results show that there is a misalignment between the prioritization done by technical professionals and the prioritization expected by business ones. This paper presents a business-driven approach to prioritize technical debt items. The research is organized into four phases: exploratory, to identify the research focus; concept verification, where the proposed approach was evaluated on a multi-case study; solution, where a design science research was conducted to develop Tracy, a framework for technical debt prioritization; and validation. Results so far show that the business-driven prioritization of technical debt items can improve the alignment and communication between the technical and business stakeholders.
{"title":"Business-Driven Technical Debt Prioritization","authors":"R. R. Almeida","doi":"10.1109/ICSME.2019.00096","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00096","url":null,"abstract":"Technical debt happens when teams take shortcuts on software development to gain short-term benefits at the cost of making future changes more expensive. Previous results show that there is a misalignment between the prioritization done by technical professionals and the prioritization expected by business ones. This paper presents a business-driven approach to prioritize technical debt items. The research is organized into four phases: exploratory, to identify the research focus; concept verification, where the proposed approach was evaluated on a multi-case study; solution, where a design science research was conducted to develop Tracy, a framework for technical debt prioritization; and validation. Results so far show that the business-driven prioritization of technical debt items can improve the alignment and communication between the technical and business stakeholders.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127779758","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}
Pub Date : 2019-08-01DOI: 10.1109/ICSME.2019.00024
David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, D. Poshyvanyk, Chris Shenefiel
Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.
{"title":"Learning to Identify Security-Related Issues Using Convolutional Neural Networks","authors":"David N. Palacio, Daniel McCrystal, Kevin Moran, Carlos Bernal-Cárdenas, D. Poshyvanyk, Chris Shenefiel","doi":"10.1109/ICSME.2019.00024","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00024","url":null,"abstract":"Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128967693","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}
Pub Date : 2019-07-31DOI: 10.1109/ICSME.2019.00028
R. R. Almeida, Christoph Treude, U. Kulesza
Technical debt is a pervasive problem in software development. Software development teams have to prioritize debt items and determine whether they should address debt or develop new features at any point in time. This paper presents "Tracy", a framework for the prioritization of technical debt using a business-driven approach built on top of business processes. The current stage of the proposed framework is at the beginning of the third phase of Design Science Research, which is usually divided into the phases of exploration, engineering, and evaluation. The exploration and engineering phases involved the participation of 49 professionals from 12 different groups of three companies. The initial evaluation shows that the presented framework is coherent in its structure and that its results contribute to business-driven decision making on technical debt prioritization.
{"title":"Tracy: A Business-Driven Technical Debt Prioritization Framework","authors":"R. R. Almeida, Christoph Treude, U. Kulesza","doi":"10.1109/ICSME.2019.00028","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00028","url":null,"abstract":"Technical debt is a pervasive problem in software development. Software development teams have to prioritize debt items and determine whether they should address debt or develop new features at any point in time. This paper presents \"Tracy\", a framework for the prioritization of technical debt using a business-driven approach built on top of business processes. The current stage of the proposed framework is at the beginning of the third phase of Design Science Research, which is usually divided into the phases of exploration, engineering, and evaluation. The exploration and engineering phases involved the participation of 49 professionals from 12 different groups of three companies. The initial evaluation shows that the presented framework is coherent in its structure and that its results contribute to business-driven decision making on technical debt prioritization.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120995613","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}
Pub Date : 2019-07-31DOI: 10.1109/ICSME.2019.00084
C. S. Yu, Christoph Treude, M. Aniche
Developers spend a large portion of their time and effort on comprehending source code. While many studies have investigated how developers approach these comprehension tasks and what factors influence their success, less is known about how developers comprehend test code specifically, despite the undisputed importance of testing. In this paper, we report on the results of an empirical study with 44 developers to understand which factors influence developers when comprehending Java test code. We measured three dependent variables: the total time spent reading a test suite, the ability to identify the overall purpose of a test suite, and the ability to produce additional test cases to extend a test suite. The main findings of our study, with several implications for future research and practitioners, are that (i) prior knowledge of the software project decreases the total reading time, (ii) experience with Java affects the proportion of time spent on the Arrange and Assert sections of test cases, (iii) experience with Java and prior knowledge of the software project positively influence the ability to produce additional test cases of certain categories, and (iv) experience with automated tests is an influential factor towards understanding and extending an automated test suite.
{"title":"Comprehending Test Code: An Empirical Study","authors":"C. S. Yu, Christoph Treude, M. Aniche","doi":"10.1109/ICSME.2019.00084","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00084","url":null,"abstract":"Developers spend a large portion of their time and effort on comprehending source code. While many studies have investigated how developers approach these comprehension tasks and what factors influence their success, less is known about how developers comprehend test code specifically, despite the undisputed importance of testing. In this paper, we report on the results of an empirical study with 44 developers to understand which factors influence developers when comprehending Java test code. We measured three dependent variables: the total time spent reading a test suite, the ability to identify the overall purpose of a test suite, and the ability to produce additional test cases to extend a test suite. The main findings of our study, with several implications for future research and practitioners, are that (i) prior knowledge of the software project decreases the total reading time, (ii) experience with Java affects the proportion of time spent on the Arrange and Assert sections of test cases, (iii) experience with Java and prior knowledge of the software project positively influence the ability to produce additional test cases of certain categories, and (iv) experience with automated tests is an influential factor towards understanding and extending an automated test suite.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123509309","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}
Pub Date : 2019-07-17DOI: 10.1109/ICSME.2019.00048
A. W. Wong, Amir Salimi, S. Chowdhury, Abram Hindle
One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.
{"title":"Syntax and Stack Overflow: A Methodology for Extracting a Corpus of Syntax Errors and Fixes","authors":"A. W. Wong, Amir Salimi, S. Chowdhury, Abram Hindle","doi":"10.1109/ICSME.2019.00048","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00048","url":null,"abstract":"One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130908645","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}
Pub Date : 2019-07-05DOI: 10.1109/ICSME.2019.00072
Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni
In this paper we introduce, MigrationMiner, an automated tool that detects code migrations performed between Java third-party library. Given a list of open source projects, the tool detects potential library migration code changes and collects the specific code fragments in which the developer replaces methods from the retired library with methods from the new library. To support the migration process, MigrationMiner collects the library documentation that is associated with every method involved in the migration. We evaluate our tool on a benchmark of manually validated library migrations. Results show that MigrationMiner achieves an accuracy of 100%. A demo video of Migration-Miner is available at https://youtu.be/sAlR1HNetXc.
{"title":"MigrationMiner: An Automated Detection Tool of Third-Party Java Library Migration at the Method Level","authors":"Hussein Alrubaye, Mohamed Wiem Mkaouer, Ali Ouni","doi":"10.1109/ICSME.2019.00072","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00072","url":null,"abstract":"In this paper we introduce, MigrationMiner, an automated tool that detects code migrations performed between Java third-party library. Given a list of open source projects, the tool detects potential library migration code changes and collects the specific code fragments in which the developer replaces methods from the retired library with methods from the new library. To support the migration process, MigrationMiner collects the library documentation that is associated with every method involved in the migration. We evaluate our tool on a benchmark of manually validated library migrations. Results show that MigrationMiner achieves an accuracy of 100%. A demo video of Migration-Miner is available at https://youtu.be/sAlR1HNetXc.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134509847","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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