Kivanç Muslu, Luke Swart, Yuriy Brun, Michael D. Ernst
Development histories can simplify some software engineering tasks, butdifferent tasks require different history granularities. For example, a history that includes every edit that resulted in compiling code is needed when searching for the cause of a regression, whereas a history that contains only changes relevant to a feature is needed for understanding the evolution of the feature. Unfortunately, today, both manual and automated history generation result in a single-granularity history. This paper introduces the concept of multi-grained development history views and the architecture of Codebase Manipulation, a tool that automatically records a fine-grained history and manages its granularity by applying granularity transformations.
{"title":"Development History Granularity Transformations (N)","authors":"Kivanç Muslu, Luke Swart, Yuriy Brun, Michael D. Ernst","doi":"10.1109/ASE.2015.53","DOIUrl":"https://doi.org/10.1109/ASE.2015.53","url":null,"abstract":"Development histories can simplify some software engineering tasks, butdifferent tasks require different history granularities. For example, a history that includes every edit that resulted in compiling code is needed when searching for the cause of a regression, whereas a history that contains only changes relevant to a feature is needed for understanding the evolution of the feature. Unfortunately, today, both manual and automated history generation result in a single-granularity history. This paper introduces the concept of multi-grained development history views and the architecture of Codebase Manipulation, a tool that automatically records a fine-grained history and manages its granularity by applying granularity transformations.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"49 1","pages":"697-702"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80701194","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}
A key challenge in implementing an efficient and precise data flow analysis is determining how to abstract the domain of values that a program variable can take on and how to update abstracted values to reflect program semantics. Such updates are performed by a transfer function and recent work by Thakur, Elder and Reps defined the bilateral algorithm for computing the most precise transfer function for a given abstract domain. In this paper, we identify and exploit the special case where abstract domains are comprised of disjoint subsets. For such domains, transfer functions computed using a customized algorithm can improve performance and in combination with symbolic modeling of block-level transfer functions improve precision as well. We implemented these algorithms in Soot and used them to perform data flow analysis on more than 100 non-trivial Java methods drawn from open source projects. Our experimental data are promising as they demonstrate that a 25-fold reduction in analysis time can be achieved and precision can be increased relative to existing methods.
{"title":"Exploiting Domain and Program Structure to Synthesize Efficient and Precise Data Flow Analyses (T)","authors":"Elena Sherman, Matthew B. Dwyer","doi":"10.1109/ASE.2015.41","DOIUrl":"https://doi.org/10.1109/ASE.2015.41","url":null,"abstract":"A key challenge in implementing an efficient and precise data flow analysis is determining how to abstract the domain of values that a program variable can take on and how to update abstracted values to reflect program semantics. Such updates are performed by a transfer function and recent work by Thakur, Elder and Reps defined the bilateral algorithm for computing the most precise transfer function for a given abstract domain. In this paper, we identify and exploit the special case where abstract domains are comprised of disjoint subsets. For such domains, transfer functions computed using a customized algorithm can improve performance and in combination with symbolic modeling of block-level transfer functions improve precision as well. We implemented these algorithms in Soot and used them to perform data flow analysis on more than 100 non-trivial Java methods drawn from open source projects. Our experimental data are promising as they demonstrate that a 25-fold reduction in analysis time can be achieved and precision can be increased relative to existing methods.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"3 1","pages":"608-618"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77837769","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}
Christopher Pietsch, Timo Kehrer, U. Kelter, Dennis Reuling, Manuel Ohrndorf
Model-based development has become a widely-used approach to implement software, e.g. for embedded systems. Models replace source code as primary executable artifacts in these cases. Software product line technologies for these domains must be able to generate models as instances of an SPL. This need is addressed among others by an implementation technology for SPLs known as delta modeling. Current approaches to delta modeling require deltas to be written manually using delta languages, and they offer only very limited support for creating and testing a network of deltas. This paper presents a new approach to delta modeling and a supporting tool suite: the abstract notion of a delta is refined to be a consistency-preserving edit script which is generated by comparing two models. The rich structure of edit scripts allows us to detect conflicts and further relations between deltas statically and to implement restructurings in delta sets such as the merging of two deltas. We illustrate the tooling using a case study.
{"title":"SiPL -- A Delta-Based Modeling Framework for Software Product Line Engineering","authors":"Christopher Pietsch, Timo Kehrer, U. Kelter, Dennis Reuling, Manuel Ohrndorf","doi":"10.1109/ASE.2015.106","DOIUrl":"https://doi.org/10.1109/ASE.2015.106","url":null,"abstract":"Model-based development has become a widely-used approach to implement software, e.g. for embedded systems. Models replace source code as primary executable artifacts in these cases. Software product line technologies for these domains must be able to generate models as instances of an SPL. This need is addressed among others by an implementation technology for SPLs known as delta modeling. Current approaches to delta modeling require deltas to be written manually using delta languages, and they offer only very limited support for creating and testing a network of deltas. This paper presents a new approach to delta modeling and a supporting tool suite: the abstract notion of a delta is refined to be a consistency-preserving edit script which is generated by comparing two models. The rich structure of edit scripts allows us to detect conflicts and further relations between deltas statically and to implement restructurings in delta sets such as the merging of two deltas. We illustrate the tooling using a case study.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"23 1","pages":"852-857"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82530726","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}
Mobile app reviews often contain useful user opinions for app developers. However, manual analysis of those reviews is challenging due to their large volume and noisynature. This paper introduces MARK, a supporting tool for review analysis of mobile apps. With MARK, an analyst can describe her interests of one or more apps via a set of keywords. MARK then lists the reviews most relevant to those keywords for further analyses. It can also draw the trends over time of the selected keywords, which might help the analyst to detect sudden changes in the related user reviews. To help the analyst describe her interests more effectively, MARK can automatically extract and rank the keywords by their associations with negative reviews, divide a large set of keywords into more cohesive subgroups, or expand a small set into a broader one.
{"title":"Tool Support for Analyzing Mobile App Reviews","authors":"P. Vu, H. Pham, Tam The Nguyen, T. Nguyen","doi":"10.1109/ASE.2015.101","DOIUrl":"https://doi.org/10.1109/ASE.2015.101","url":null,"abstract":"Mobile app reviews often contain useful user opinions for app developers. However, manual analysis of those reviews is challenging due to their large volume and noisynature. This paper introduces MARK, a supporting tool for review analysis of mobile apps. With MARK, an analyst can describe her interests of one or more apps via a set of keywords. MARK then lists the reviews most relevant to those keywords for further analyses. It can also draw the trends over time of the selected keywords, which might help the analyst to detect sudden changes in the related user reviews. To help the analyst describe her interests more effectively, MARK can automatically extract and rank the keywords by their associations with negative reviews, divide a large set of keywords into more cohesive subgroups, or expand a small set into a broader one.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"47 1","pages":"789-794"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86800865","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}
Hiroyuki Fudaba, Yusuke Oda, Koichi Akabe, Graham Neubig, Hideaki Hata, S. Sakti, T. Toda, Satoshi Nakamura
Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.
{"title":"Pseudogen: A Tool to Automatically Generate Pseudo-Code from Source Code","authors":"Hiroyuki Fudaba, Yusuke Oda, Koichi Akabe, Graham Neubig, Hideaki Hata, S. Sakti, T. Toda, Satoshi Nakamura","doi":"10.1109/ASE.2015.107","DOIUrl":"https://doi.org/10.1109/ASE.2015.107","url":null,"abstract":"Understanding the behavior of source code written in an unfamiliar programming language is difficult. One way to aid understanding of difficult code is to add corresponding pseudo-code, which describes in detail the workings of the code in a natural language such as English. In spite of its usefulness, most source code does not have corresponding pseudo-code because it is tedious to create. This paper demonstrates a tool Pseudogen that makes it possible to automatically generate pseudo-code from source code using statistical machine translation (SMT). Pseudogen currently supports generation of English or Japanese pseudo-code from Python source code, and the SMT framework makes it easy for users to create new generators for their preferred source code/pseudo-code pairs.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"104 1","pages":"824-829"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79962615","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}
Dynamic specification mining extracts candidate specifications from logs of execution traces. Existing algorithms differ in the kinds of traces they take as input and in the kinds of candidate specification they present as output. One challenge common to all approaches relates to the faithfulness of the mining results: how can we be confident that the extracted specifications faithfully characterize the program we investigate? Since producing and analyzing traces is costly, how would we know we have seen enough traces? And, how would we know we have not wasted resources and seen too many of them?In this paper we address these important questions by presenting a novel, black box, probabilistic framework based on a notion of log completeness, and by applying it to three different well-known specification mining algorithms from the literature: k-Tails, Synoptic, and mining of scenario-based triggers and effects. Extensive evaluation over 24 models taken from 9 different sources shows the soundness, generalizability, and usefulness of the framework and its contribution to the state-of-the-art in dynamic specification mining.
{"title":"Have We Seen Enough Traces? (T)","authors":"Hila Cohen, S. Maoz","doi":"10.1109/ASE.2015.62","DOIUrl":"https://doi.org/10.1109/ASE.2015.62","url":null,"abstract":"Dynamic specification mining extracts candidate specifications from logs of execution traces. Existing algorithms differ in the kinds of traces they take as input and in the kinds of candidate specification they present as output. One challenge common to all approaches relates to the faithfulness of the mining results: how can we be confident that the extracted specifications faithfully characterize the program we investigate? Since producing and analyzing traces is costly, how would we know we have seen enough traces? And, how would we know we have not wasted resources and seen too many of them?In this paper we address these important questions by presenting a novel, black box, probabilistic framework based on a notion of log completeness, and by applying it to three different well-known specification mining algorithms from the literature: k-Tails, Synoptic, and mining of scenario-based triggers and effects. Extensive evaluation over 24 models taken from 9 different sources shows the soundness, generalizability, and usefulness of the framework and its contribution to the state-of-the-art in dynamic specification mining.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"2 1","pages":"93-103"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76868846","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 : 2015-11-09DOI: 10.1016/j.scico.2017.06.011
Raffi Khatchadourian, A. Rashid, H. Masuhara, Takuya Watanabe
{"title":"Detecting Broken Pointcuts Using Structural Commonality and Degree of Interest (N)","authors":"Raffi Khatchadourian, A. Rashid, H. Masuhara, Takuya Watanabe","doi":"10.1016/j.scico.2017.06.011","DOIUrl":"https://doi.org/10.1016/j.scico.2017.06.011","url":null,"abstract":"","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"51 1","pages":"641-646"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86131633","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}
When developing the user interface code of a mobile application, in practice a big gap exists between the digital conceptual drawings of graphic artists and working user interface code. Currently, programmers bridge this gap manually, by reimplementing the conceptual drawings in code, which is cumbersome and expensive. To bridge this gap, we introduce the first technique to automatically Reverse Engineer Mobile Application User Interfaces (REMAUI). On a given input bitmap REMAUI identifies user interface elements such as images, texts, containers, and lists, via computer vision and optical character recognition (OCR) techniques. In our experiments on 488 screenshots of over 100 popular third-party Android and iOS applications, REMAUI-generated user interfaces were similar to the originals, both pixel-by-pixel and in terms of their runtime user interface hierarchies. REMAUI's average overall runtime on a standard desktop computer was 9 seconds.
{"title":"Reverse Engineering Mobile Application User Interfaces with REMAUI (T)","authors":"T. Nguyen, Christoph Csallner","doi":"10.1109/ASE.2015.32","DOIUrl":"https://doi.org/10.1109/ASE.2015.32","url":null,"abstract":"When developing the user interface code of a mobile application, in practice a big gap exists between the digital conceptual drawings of graphic artists and working user interface code. Currently, programmers bridge this gap manually, by reimplementing the conceptual drawings in code, which is cumbersome and expensive. To bridge this gap, we introduce the first technique to automatically Reverse Engineer Mobile Application User Interfaces (REMAUI). On a given input bitmap REMAUI identifies user interface elements such as images, texts, containers, and lists, via computer vision and optical character recognition (OCR) techniques. In our experiments on 488 screenshots of over 100 popular third-party Android and iOS applications, REMAUI-generated user interfaces were similar to the originals, both pixel-by-pixel and in terms of their runtime user interface hierarchies. REMAUI's average overall runtime on a standard desktop computer was 9 seconds.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"36 1","pages":"248-259"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79349571","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}
RESTful APIs are often manually documented. As a result, the process of maintaining the documentation of RESTful APIs is both expensive and error-prone. In this demonstration paper, we present SpyREST as an automated software as a service tool that can be used to document RESTful APIs. SpyREST leverages an HTTP Proxy server to intercept real API calls to automatically collect and generate RESTful API documentation by processing HTTP traffic involved in API calls. SpyREST provides an automated yet customizable example based documentation solution for RESTful APIs. RESTful API developers can use SpyREST to automatically generate and maintain updated API documentation.
{"title":"SpyREST in Action: An Automated RESTful API Documentation Tool","authors":"S. Sohan, C. Anslow, F. Maurer","doi":"10.1109/ASE.2015.92","DOIUrl":"https://doi.org/10.1109/ASE.2015.92","url":null,"abstract":"RESTful APIs are often manually documented. As a result, the process of maintaining the documentation of RESTful APIs is both expensive and error-prone. In this demonstration paper, we present SpyREST as an automated software as a service tool that can be used to document RESTful APIs. SpyREST leverages an HTTP Proxy server to intercept real API calls to automatically collect and generate RESTful API documentation by processing HTTP traffic involved in API calls. SpyREST provides an automated yet customizable example based documentation solution for RESTful APIs. RESTful API developers can use SpyREST to automatically generate and maintain updated API documentation.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"89 1","pages":"813-818"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84902311","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}
Santiago Vargas-Baldrich, M. Vásquez, D. Poshyvanyk
Several open and closed source repositories group software systems and libraries to allow members of particular organizations or the open source community to take advantage of them. However, to make this possible, it is necessary to have effective ways of searching and browsing the repositories. Software tagging is the process of assigning terms (i.e., tags or labels) to software assets in order to describe features and internal details, making the task of understanding software easier and potentially browsing and searching through a repository more effective. We present Sally, an automatic software tagging approach that is able to produce meaningful tags for Maven-based software projects by analyzing their bytecode and dependency relations without any special requirements from developers. We compared tags generated by Sally to the ones in two widely used online repositories, and the tags generated by a state-of-the-art categorization approach. The results suggest that Sally is able to generate expressive tags without relying on machine learning-based models.
{"title":"Automated Tagging of Software Projects Using Bytecode and Dependencies (N)","authors":"Santiago Vargas-Baldrich, M. Vásquez, D. Poshyvanyk","doi":"10.1109/ASE.2015.38","DOIUrl":"https://doi.org/10.1109/ASE.2015.38","url":null,"abstract":"Several open and closed source repositories group software systems and libraries to allow members of particular organizations or the open source community to take advantage of them. However, to make this possible, it is necessary to have effective ways of searching and browsing the repositories. Software tagging is the process of assigning terms (i.e., tags or labels) to software assets in order to describe features and internal details, making the task of understanding software easier and potentially browsing and searching through a repository more effective. We present Sally, an automatic software tagging approach that is able to produce meaningful tags for Maven-based software projects by analyzing their bytecode and dependency relations without any special requirements from developers. We compared tags generated by Sally to the ones in two widely used online repositories, and the tags generated by a state-of-the-art categorization approach. The results suggest that Sally is able to generate expressive tags without relying on machine learning-based models.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"49 1","pages":"289-294"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85321901","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：