Test smells are, analogously to code smells, defined as the characteristics exhibited by poorly designed unit tests. Their negative impact on test effectiveness, understanding, and maintenance has been demonstrated by several empirical studies. However, the scope of these studies has been limited mostly to Java in combination with the JUnit testing framework. Results for other language and framework combinations are –-despite their prevalence in practice–- few and far between, which might skew our understanding of test smells. The combination of Scala and ScalaTest, for instance, offers more comprehensive means for defining and reusing test fixtures, thereby possibly reducing the diffusion and perception of fixture-related test smells. This paper therefore reports on two empirical studies conducted for this combination. In the first study, we analyse the tests of 164 open-source Scala projects hosted on GitHub for the diffusion of test smells. This required the transposition of their original definition to this new context, and the implementation of a tool (SOCRATES) for their automated detection. In the second study, we assess the perception and the ability of 14 Scala developers to identify test smells. For this context, our results show (i) that test smells have a low diffusion across test classes, (ii) that the most frequently occurring test smells are LazyTest, EagerTest, and AssertionRoulette, and (iii) that many developers were able to perceive but not to identify the smells.
{"title":"Assessing Diffusion and Perception of Test Smells in Scala Projects","authors":"Jonas De Bleser, Dario Di Nucci, Coen De Roover","doi":"10.1109/MSR.2019.00072","DOIUrl":"https://doi.org/10.1109/MSR.2019.00072","url":null,"abstract":"Test smells are, analogously to code smells, defined as the characteristics exhibited by poorly designed unit tests. Their negative impact on test effectiveness, understanding, and maintenance has been demonstrated by several empirical studies. However, the scope of these studies has been limited mostly to Java in combination with the JUnit testing framework. Results for other language and framework combinations are –-despite their prevalence in practice–- few and far between, which might skew our understanding of test smells. The combination of Scala and ScalaTest, for instance, offers more comprehensive means for defining and reusing test fixtures, thereby possibly reducing the diffusion and perception of fixture-related test smells. This paper therefore reports on two empirical studies conducted for this combination. In the first study, we analyse the tests of 164 open-source Scala projects hosted on GitHub for the diffusion of test smells. This required the transposition of their original definition to this new context, and the implementation of a tool (SOCRATES) for their automated detection. In the second study, we assess the perception and the ability of 14 Scala developers to identify test smells. For this context, our results show (i) that test smells have a low diffusion across test classes, (ii) that the most frequently occurring test smells are LazyTest, EagerTest, and AssertionRoulette, and (iii) that many developers were able to perceive but not to identify the smells.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"36 1","pages":"457-467"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74805260","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}
Stack Overflow is home to a large number of technical questions and answers. These answers also include comments from the community and other users about the answer's validity. Such comments may point to flaws in the posted answer or may indicate deprecated code that is no longer valid due to API changes. In this paper, we explore how comments affect answer updates on Stack Overflow, using the SOTorrent dataset. Our results show that a large number of answers on Stack Overflow are not updated, even when they receive comments that warrant an update. Our results can be used to build recommender systems that automatically identify answers that require updating, or even automatically update answers as needed.
{"title":"Analyzing Comment-Induced Updates on Stack Overflow","authors":"Abhishek Soni, Sarah Nadi","doi":"10.1109/MSR.2019.00044","DOIUrl":"https://doi.org/10.1109/MSR.2019.00044","url":null,"abstract":"Stack Overflow is home to a large number of technical questions and answers. These answers also include comments from the community and other users about the answer's validity. Such comments may point to flaws in the posted answer or may indicate deprecated code that is no longer valid due to API changes. In this paper, we explore how comments affect answer updates on Stack Overflow, using the SOTorrent dataset. Our results show that a large number of answers on Stack Overflow are not updated, even when they receive comments that warrant an update. Our results can be used to build recommender systems that automatically identify answers that require updating, or even automatically update answers as needed.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"25 1","pages":"220-224"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82252237","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}
Stanislav Chren, Radoslav Micko, Barbora Buhnova, B. Rossi
Reliability is an essential attribute of mission-and safety-critical systems. Software Reliability Growth Models (SRGMs) are regression-based models that use historical failure data to predict the reliability-related parameters. At the moment, there is no dedicated tool available that would be able to cover the whole process of SRGMs data preparation and application from issue repositories, discouraging replications and reuse in other projects. In this paper, we introduce STRAIT, a free and open-source tool for automatic software reliability growth analysis which utilizes data from issue repositories. STRAIT features downloading, filtering and processing of data from provided issue repositories for use in multiple SRGMs, suggesting the best fitting SRGM with multiple data snapshots to consider software evolution. The tool is designed to be highly extensible, in terms of additional issue repositories, SRGMs, and new data filtering and processing options. Quality engineers can use STRAIT for the evaluation of their software systems. The research community can use STRAIT for empirical studies which involve evaluation of new SRGMs or comparison of multiple SRGMs.
{"title":"STRAIT: A Tool for Automated Software Reliability Growth Analysis","authors":"Stanislav Chren, Radoslav Micko, Barbora Buhnova, B. Rossi","doi":"10.1109/MSR.2019.00025","DOIUrl":"https://doi.org/10.1109/MSR.2019.00025","url":null,"abstract":"Reliability is an essential attribute of mission-and safety-critical systems. Software Reliability Growth Models (SRGMs) are regression-based models that use historical failure data to predict the reliability-related parameters. At the moment, there is no dedicated tool available that would be able to cover the whole process of SRGMs data preparation and application from issue repositories, discouraging replications and reuse in other projects. In this paper, we introduce STRAIT, a free and open-source tool for automatic software reliability growth analysis which utilizes data from issue repositories. STRAIT features downloading, filtering and processing of data from provided issue repositories for use in multiple SRGMs, suggesting the best fitting SRGM with multiple data snapshots to consider software evolution. The tool is designed to be highly extensible, in terms of additional issue repositories, SRGMs, and new data filtering and processing options. Quality engineers can use STRAIT for the evaluation of their software systems. The research community can use STRAIT for empirical studies which involve evaluation of new SRGMs or comparison of multiple SRGMs.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"63 1","pages":"105-110"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73864545","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}
Word embeddings produced by the word2vec algorithm provide us with a strong mechanism to discover relationships between the words based on the degree to which they are contextually related to one another. In and of itself, algorithms like word2vec do not give us a mechanism to impose ordering constraints on the embedded word representations. Our main goal in this paper is to exploit the semantic word vectors obtained from word2vec in such a way that allows for the ordering constraints to be invoked on them when comparing a sequence of words in a query with a sequence of words in a file for source code retrieval. These ordering constraints employ the logic of Markov Random Fields (MRF), a framework used previously to enhance the precision of the source-code retrieval engines based on the Bag-of-Words (BoW) assumption. The work we present here demonstrates that by combining word2vec with the power of MRF, it is possible to achieve improvements between 6% and 30% in retrieval accuracy over the best results that can be obtained with the more traditional applications of MRF to representations based on term and term-term frequencies. The performance improvement was 30% for the Java AspectJ repository using only the titles of the bug reports provided by iBUGS, and 6% for the case of the Eclipse repository using titles as well as descriptions of the bug reports provided by BUGLinks.
{"title":"SCOR: Source Code Retrieval with Semantics and Order","authors":"Shayan A. Akbar, A. Kak","doi":"10.1109/MSR.2019.00012","DOIUrl":"https://doi.org/10.1109/MSR.2019.00012","url":null,"abstract":"Word embeddings produced by the word2vec algorithm provide us with a strong mechanism to discover relationships between the words based on the degree to which they are contextually related to one another. In and of itself, algorithms like word2vec do not give us a mechanism to impose ordering constraints on the embedded word representations. Our main goal in this paper is to exploit the semantic word vectors obtained from word2vec in such a way that allows for the ordering constraints to be invoked on them when comparing a sequence of words in a query with a sequence of words in a file for source code retrieval. These ordering constraints employ the logic of Markov Random Fields (MRF), a framework used previously to enhance the precision of the source-code retrieval engines based on the Bag-of-Words (BoW) assumption. The work we present here demonstrates that by combining word2vec with the power of MRF, it is possible to achieve improvements between 6% and 30% in retrieval accuracy over the best results that can be obtained with the more traditional applications of MRF to representations based on term and term-term frequencies. The performance improvement was 30% for the Java AspectJ repository using only the titles of the bug reports provided by iBUGS, and 6% for the case of the Eclipse repository using titles as well as descriptions of the bug reports provided by BUGLinks.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"205 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77476392","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}
Uriel Campos, Guilherme Smethurst, João Pedro Moraes, R. Bonifácio, G. Pinto
Programming code snippets readily available on platforms such as StackOverflow are undoubtedly useful for software engineers. Unfortunately, these code snippets might contain issues such as deprecated, misused, or even buggy code. These issues could pass unattended, if developers do not have adequate knowledge, time, or tool support to catch them. In this work we expand the understanding of such issues (or the so called "violations") hidden in code snippets written in JavaScript, the programming language with the highest number of questions on StackOverflow. To characterize the violations, we extracted 336k code snippets from answers to JavaScript questions on StackOverflow and statically analyzed them using ESLinter, a JavaScript linter. We discovered that there is no single JavaScript code snippet without a rule violation. On average, our studied code snippets have 11 violations, but we found instances of more than 200 violations. In particular, rules related to stylistic issues are by far the most violated ones (82.9% of the violations pertain to this category). Possible errors, which developers might be more interested in, represent only 0.1% of the violations. Finally, we found a small fraction of code snippets flagged with possible errors being reused on actual GitHub software projects. Indeed, one single code snippet with possible errors was reused 1,261 times.
{"title":"Mining Rule Violations in JavaScript Code Snippets","authors":"Uriel Campos, Guilherme Smethurst, João Pedro Moraes, R. Bonifácio, G. Pinto","doi":"10.1109/MSR.2019.00039","DOIUrl":"https://doi.org/10.1109/MSR.2019.00039","url":null,"abstract":"Programming code snippets readily available on platforms such as StackOverflow are undoubtedly useful for software engineers. Unfortunately, these code snippets might contain issues such as deprecated, misused, or even buggy code. These issues could pass unattended, if developers do not have adequate knowledge, time, or tool support to catch them. In this work we expand the understanding of such issues (or the so called \"violations\") hidden in code snippets written in JavaScript, the programming language with the highest number of questions on StackOverflow. To characterize the violations, we extracted 336k code snippets from answers to JavaScript questions on StackOverflow and statically analyzed them using ESLinter, a JavaScript linter. We discovered that there is no single JavaScript code snippet without a rule violation. On average, our studied code snippets have 11 violations, but we found instances of more than 200 violations. In particular, rules related to stylistic issues are by far the most violated ones (82.9% of the violations pertain to this category). Possible errors, which developers might be more interested in, represent only 0.1% of the violations. Finally, we found a small fraction of code snippets flagged with possible errors being reused on actual GitHub software projects. Indeed, one single code snippet with possible errors was reused 1,261 times.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"138 1","pages":"195-199"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77489490","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}
Despite being the most popular question and answer website for software developers, answers posted on Stack Overflow (SO) are susceptible to contain Python-related insecure coding practices. A systematic analysis on how frequently insecure coding practices appear in SO answers can help the SO community assess the prevalence of insecure Python code blocks in SO. An insecure coding practice is recurrent use of insecure coding patterns in Python. We conduct an empirical study using 529,054 code blocks collected from Python-related 44,966 answers posted on SO. We observe 7.1% of the 44,966 Python-related answers to include at least one insecure coding practice. The most frequently occurring insecure coding practice is code injection. We observe 9.8% of the 7,444 accepted answers to include at least one insecure code block. We also find user reputation not to relate with the presence of insecure code blocks, suggesting that both high and low-reputed users are likely to introduce insecure code blocks.
{"title":"Snakes in Paradise?: Insecure Python-Related Coding Practices in Stack Overflow","authors":"A. Rahman, Effat Farhana, Nasif Imtiaz","doi":"10.1109/MSR.2019.00040","DOIUrl":"https://doi.org/10.1109/MSR.2019.00040","url":null,"abstract":"Despite being the most popular question and answer website for software developers, answers posted on Stack Overflow (SO) are susceptible to contain Python-related insecure coding practices. A systematic analysis on how frequently insecure coding practices appear in SO answers can help the SO community assess the prevalence of insecure Python code blocks in SO. An insecure coding practice is recurrent use of insecure coding patterns in Python. We conduct an empirical study using 529,054 code blocks collected from Python-related 44,966 answers posted on SO. We observe 7.1% of the 44,966 Python-related answers to include at least one insecure coding practice. The most frequently occurring insecure coding practice is code injection. We observe 9.8% of the 7,444 accepted answers to include at least one insecure code block. We also find user reputation not to relate with the presence of insecure code blocks, suggesting that both high and low-reputed users are likely to introduce insecure code blocks.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"32 1","pages":"200-204"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86566099","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}
This paper presents the GreenSource infrastructure: a large body of open source code, executable Android applications, and curated dataset containing energy code metrics. The dataset contains energy metrics obtained by both static analysing the applications' source code and by executing them with available test inputs. To automate the execution of the applications we developed the AnaDroid tool which instruments its code, compiles and executes it with test inputs in any Android device, while collecting energy metrics. GreenSource includes all Android applications included in the MUSE Java source code repository, while AnaDroid implements all Android's energy greedy features described in the literature, GreenSource aims at characterizing energy consumption in the Android ecosystem, providing both Android developers and researchers a setting to reason about energy efficient Android software development.
{"title":"GreenSource: A Large-Scale Collection of Android Code, Tests and Energy Metrics","authors":"Rui Rua, Marco Couto, J. Saraiva","doi":"10.1109/MSR.2019.00035","DOIUrl":"https://doi.org/10.1109/MSR.2019.00035","url":null,"abstract":"This paper presents the GreenSource infrastructure: a large body of open source code, executable Android applications, and curated dataset containing energy code metrics. The dataset contains energy metrics obtained by both static analysing the applications' source code and by executing them with available test inputs. To automate the execution of the applications we developed the AnaDroid tool which instruments its code, compiles and executes it with test inputs in any Android device, while collecting energy metrics. GreenSource includes all Android applications included in the MUSE Java source code repository, while AnaDroid implements all Android's energy greedy features described in the literature, GreenSource aims at characterizing energy consumption in the Android ecosystem, providing both Android developers and researchers a setting to reason about energy efficient Android software development.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"38 1","pages":"176-180"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84806045","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}
The rise of mobile apps as new software systems led to the emergence of new development requirements regarding performance. Development practices that do not respect these requirements can seriously hinder app performances and impair user experience, they qualify as code smells. Mobile code smells are generally associated with inexperienced developers who lack knowledge about the framework guidelines. However, this assumption remains unverified and there is no evidence about the role played by developers in the accrual of mobile code smells. In this paper, we therefore study the contributions of developers related to Android code smells. To support this study, we propose Sniffer, an open-source toolkit that mines Git repositories to extract developers' contributions as code smell histories. Using Sniffer, we analysed 255k commits from the change history of 324 Android apps. We found that the ownership of code smells is spread across developers regardless of their seniority. There are no distinct groups of code smell introducers and removers. Developers who introduce and remove code smells are mostly the same.
{"title":"The Rise of Android Code Smells: Who is to Blame?","authors":"Sarra Habchi, Naouel Moha, Romain Rouvoy","doi":"10.1109/MSR.2019.00071","DOIUrl":"https://doi.org/10.1109/MSR.2019.00071","url":null,"abstract":"The rise of mobile apps as new software systems led to the emergence of new development requirements regarding performance. Development practices that do not respect these requirements can seriously hinder app performances and impair user experience, they qualify as code smells. Mobile code smells are generally associated with inexperienced developers who lack knowledge about the framework guidelines. However, this assumption remains unverified and there is no evidence about the role played by developers in the accrual of mobile code smells. In this paper, we therefore study the contributions of developers related to Android code smells. To support this study, we propose Sniffer, an open-source toolkit that mines Git repositories to extract developers' contributions as code smell histories. Using Sniffer, we analysed 255k commits from the change history of 324 Android apps. We found that the ownership of code smells is spread across developers regardless of their seniority. There are no distinct groups of code smell introducers and removers. Developers who introduce and remove code smells are mostly the same.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"53 1","pages":"445-456"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86165333","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}
Developers frequently copy code snippets from publicly-available resources such as Stack Overflow (SO). While this may lead to a 'quick fix' for a development problem, little is known about how these copied code snippets affect the code quality of the recipient application, or how the quality of the recipient classes subsequently evolve over the time of the project. This has an impact on whether such code copying should be encouraged, and how classes that receive such code snippets should be monitored during evolution. To investigate this issue, we used instances from the SOTorrent database where Java snippets had been copied from Stack Overflow into GitHub projects. In each case, we measured the quality of the recipient class just prior to the addition of the snippet, immediately after the addition of the snippet, and at a later stage in the project. Our goal was to determine if the addition of the snippet caused quality to improve or deteriorate, and what the long-term implications were for the quality of the recipient class. Code quality was measured using the cohesion metrics Low-level Similarity-based Class Cohesion (LSCC) and Class Cohesion (CC). Over a random sample of 378 classes that received code snippets copied from Stack Overflow to GitHub, we found that in almost 70% of the cases where the copied snippet affected cohesion, the effect was to reduce the cohesion of the recipient class. Furthermore, this deterioration in cohesion tends to persist in the subsequent evolution of the recipient class. In over 70% of cases the recipient class never fully regained the cohesion it lost in receiving the snippet. These results suggest that when copying code snippets from external repositories, more attention should be paid to integrating the code with the recipient class.
{"title":"Impact of Stack Overflow Code Snippets on Software Cohesion: A Preliminary Study","authors":"Mashal Ahmad, M. Cinnéide","doi":"10.1109/MSR.2019.00050","DOIUrl":"https://doi.org/10.1109/MSR.2019.00050","url":null,"abstract":"Developers frequently copy code snippets from publicly-available resources such as Stack Overflow (SO). While this may lead to a 'quick fix' for a development problem, little is known about how these copied code snippets affect the code quality of the recipient application, or how the quality of the recipient classes subsequently evolve over the time of the project. This has an impact on whether such code copying should be encouraged, and how classes that receive such code snippets should be monitored during evolution. To investigate this issue, we used instances from the SOTorrent database where Java snippets had been copied from Stack Overflow into GitHub projects. In each case, we measured the quality of the recipient class just prior to the addition of the snippet, immediately after the addition of the snippet, and at a later stage in the project. Our goal was to determine if the addition of the snippet caused quality to improve or deteriorate, and what the long-term implications were for the quality of the recipient class. Code quality was measured using the cohesion metrics Low-level Similarity-based Class Cohesion (LSCC) and Class Cohesion (CC). Over a random sample of 378 classes that received code snippets copied from Stack Overflow to GitHub, we found that in almost 70% of the cases where the copied snippet affected cohesion, the effect was to reduce the cohesion of the recipient class. Furthermore, this deterioration in cohesion tends to persist in the subsequent evolution of the recipient class. In over 70% of cases the recipient class never fully regained the cohesion it lost in receiving the snippet. These results suggest that when copying code snippets from external repositories, more attention should be paid to integrating the code with the recipient class.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"71 1","pages":"250-254"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90363369","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}
The success of question and answer (Q&A) websites attracts massive user-generated content for using and learning APIs, which easily leads to information overload: many questions for APIs have a large number of answers containing useful and irrelevant information, and cannot all be consumed by developers. In this work, we develop DeepTip, a novel deep learning-based approach using different Convolutional Neural Network architectures, to extract short practical and useful tips from developer answers. Our extensive empirical experiments prove that DeepTip can extract useful tips from a large corpus of answers to questions with high precision (i.e., avg. 0.854) and coverage (i.e., 0.94), and it outperforms two state-of-the-art baselines by up to 56.7% and 162%, respectively, in terms of Precision. Furthermore, qualitatively, a user study is conducted with real Stack Overflow users and its results confirm that tip extraction is useful and our approach generates high-quality tips.
{"title":"Extracting API Tips from Developer Question and Answer Websites","authors":"Shaohua Wang, Nhathai Phan, Yan Wang, Yong Zhao","doi":"10.1109/MSR.2019.00058","DOIUrl":"https://doi.org/10.1109/MSR.2019.00058","url":null,"abstract":"The success of question and answer (Q&A) websites attracts massive user-generated content for using and learning APIs, which easily leads to information overload: many questions for APIs have a large number of answers containing useful and irrelevant information, and cannot all be consumed by developers. In this work, we develop DeepTip, a novel deep learning-based approach using different Convolutional Neural Network architectures, to extract short practical and useful tips from developer answers. Our extensive empirical experiments prove that DeepTip can extract useful tips from a large corpus of answers to questions with high precision (i.e., avg. 0.854) and coverage (i.e., 0.94), and it outperforms two state-of-the-art baselines by up to 56.7% and 162%, respectively, in terms of Precision. Furthermore, qualitatively, a user study is conducted with real Stack Overflow users and its results confirm that tip extraction is useful and our approach generates high-quality tips.","PeriodicalId":6706,"journal":{"name":"2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)","volume":"1 1","pages":"321-332"},"PeriodicalIF":0.0,"publicationDate":"2019-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77353009","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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