Patrick W. Koch, Konstantin Schekotihin, D. Jannach, Birgit Hofer, F. Wotawa
Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.
{"title":"Combining Spreadsheet Smells for Improved Fault Prediction","authors":"Patrick W. Koch, Konstantin Schekotihin, D. Jannach, Birgit Hofer, F. Wotawa","doi":"10.1145/3183399.3183402","DOIUrl":"https://doi.org/10.1145/3183399.3183402","url":null,"abstract":"Spreadsheets are commonly used in organizations as a programming tool for business-related calculations and decision making. Since faults in spreadsheets can have severe business impacts, a number of approaches from general software engineering have been applied to spreadsheets in recent years, among them the concept of code smells. Smells can in particular be used for the task of fault prediction. An analysis of existing spreadsheet smells, however, revealed that the predictive power of individual smells can be limited. In this work we therefore propose a machine learning based approach which combines the predictions of individual smells by using an AdaBoost ensemble classifier. Experiments on two public datasets containing real-world spreadsheet faults show significant improvements in terms of fault prediction accuracy.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127483021","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}
Research in the area of Cyber-Physical Systems (CPS) is hampered by the lack of available project environments in which to explore open challenges and to propose and rigorously evaluate solutions. In this "New Ideas and Emerging Results" paper we introduce a CPS research incubator – based upon a system, and its associated project environment, for managing and coordinating the flight of small Unmanned Aerial Systems (sUAS). The research incubator provides a new community resource, making available diverse, high-quality project artifacts produced across multiple releases of a safety-critical CPS. It enables researchers to experiment with their own novel solutions within a fully-executable runtime environment that supports both high-fidelity sUAS simulations as well as physical sUAS. Early collaborators from the software engineering community have shown broad and enthusiastic support for the project and its role as a research incubator, and have indicated their intention to leverage the environment to address their own research areas of goal modeling, runtime adaptation, safety-assurance, and software evolution.
{"title":"Dronology: An Incubator for Cyber-Physical Systems Research","authors":"J. Cleland-Huang, Michael Vierhauser, Sean Bayley","doi":"10.1145/3183399.3183408","DOIUrl":"https://doi.org/10.1145/3183399.3183408","url":null,"abstract":"Research in the area of Cyber-Physical Systems (CPS) is hampered by the lack of available project environments in which to explore open challenges and to propose and rigorously evaluate solutions. In this \"New Ideas and Emerging Results\" paper we introduce a CPS research incubator – based upon a system, and its associated project environment, for managing and coordinating the flight of small Unmanned Aerial Systems (sUAS). The research incubator provides a new community resource, making available diverse, high-quality project artifacts produced across multiple releases of a safety-critical CPS. It enables researchers to experiment with their own novel solutions within a fully-executable runtime environment that supports both high-fidelity sUAS simulations as well as physical sUAS. Early collaborators from the software engineering community have shown broad and enthusiastic support for the project and its role as a research incubator, and have indicated their intention to leverage the environment to address their own research areas of goal modeling, runtime adaptation, safety-assurance, and software evolution.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121291339","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}
Translating a program written in one programming language to another can be useful for software development tasks that need functionality implementations in different languages. Although past studies have considered this problem, they may be either specific to the language grammars, or specific to certain kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a new approach to automatically learn cross-language representations for various kinds of structural code elements that may be used for program translation. Our key idea is two folded: First, we normalize and enrich code token streams with additional structural and semantic information, and train cross-language vector representations for the tokens (a.k.a. shared embeddings based on word2vec, a neural-network-based technique for producing word embeddings; Second, hierarchically from bottom up, we construct shared embeddings for code elements of higher levels of granularity (e.g., expressions, statements, methods) from the embeddings for their constituents, and then build mappings among code elements across languages based on similarities among embeddings. Our preliminary evaluations on about 40,000 Java and C# source files from 9 software projects show that our approach can automatically learn shared embeddings for various code elements in different languages and identify their cross-language mappings with reasonable Mean Average Precision scores. When compared with an existing tool for mapping library API methods, our approach identifies many more mappings accurately. The mapping results and code can be accessed at https://github.com/bdqnghi/hierarchical-programming-language-mapping) We believe that our idea for learning cross-language vector representations with code structural information can be a useful step towards automated program translation.
对于需要用不同语言实现功能的软件开发任务，将用一种编程语言编写的程序翻译成另一种编程语言是很有用的。虽然过去的研究已经考虑了这个问题，但它们可能是特定于语言语法的，或者特定于某些类型的代码元素(例如，记号、短语、API使用)。本文提出了一种自动学习可用于程序翻译的各种结构代码元素的跨语言表示的新方法。我们的关键思想有两个方面:首先，我们用额外的结构和语义信息规范化和丰富代码标记流，并训练标记的跨语言向量表示(即基于word2vec的共享嵌入，这是一种基于神经网络的生成词嵌入的技术;其次，从下至上的层次结构，我们为更高粒度级别的代码元素(例如表达式、语句、方法)构建共享嵌入，然后基于嵌入之间的相似性在不同语言的代码元素之间构建映射。我们对来自9个软件项目的大约40,000个Java和c#源文件的初步评估表明，我们的方法可以自动学习不同语言中各种代码元素的共享嵌入，并以合理的Mean Average Precision分数识别它们的跨语言映射。与现有的映射库API方法的工具相比，我们的方法可以准确地识别更多的映射。映射结果和代码可以访问https://github.com/bdqnghi/hierarchical-programming-language-mapping)我们相信，我们用代码结构信息学习跨语言向量表示的想法可以成为实现自动程序翻译的有用步骤。
{"title":"Hierarchical Learning of Cross-Language Mappings Through Distributed Vector Representations for Code","authors":"Nghi D. Q. Bui, Lingxiao Jiang","doi":"10.1145/3183399.3183427","DOIUrl":"https://doi.org/10.1145/3183399.3183427","url":null,"abstract":"Translating a program written in one programming language to another can be useful for software development tasks that need functionality implementations in different languages. Although past studies have considered this problem, they may be either specific to the language grammars, or specific to certain kinds of code elements (e.g., tokens, phrases, API uses). This paper proposes a new approach to automatically learn cross-language representations for various kinds of structural code elements that may be used for program translation. Our key idea is two folded: First, we normalize and enrich code token streams with additional structural and semantic information, and train cross-language vector representations for the tokens (a.k.a. shared embeddings based on word2vec, a neural-network-based technique for producing word embeddings; Second, hierarchically from bottom up, we construct shared embeddings for code elements of higher levels of granularity (e.g., expressions, statements, methods) from the embeddings for their constituents, and then build mappings among code elements across languages based on similarities among embeddings. Our preliminary evaluations on about 40,000 Java and C# source files from 9 software projects show that our approach can automatically learn shared embeddings for various code elements in different languages and identify their cross-language mappings with reasonable Mean Average Precision scores. When compared with an existing tool for mapping library API methods, our approach identifies many more mappings accurately. The mapping results and code can be accessed at https://github.com/bdqnghi/hierarchical-programming-language-mapping) We believe that our idea for learning cross-language vector representations with code structural information can be a useful step towards automated program translation.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121954209","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}
Recent research provides evidence that effective communication in collaborative software development has significant impact on the software development lifecycle. Although related qualitative and quantitative studies point out textual characteristics of well-formed messages, the underlying semantics of the intertwined linguistic structures still remain largely misinterpreted or ignored. Especially, regarding quality of code reviews the importance of thorough feedback, and explicit rationale is often mentioned but rarely linked with related linguistic features. As a first step towards addressing this shortcoming, we propose grounding these studies on theories of linguistics. We particularly focus on linguistic structures of coherent speech and explain how they can be exploited in practice. We reflect on related approaches and examine through a preliminary study on four open source projects, possible links between existing findings and the directions we suggest for detecting textual features of useful code reviews.
{"title":"Code Review Comments: Language Matters","authors":"V. Efstathiou, D. Spinellis","doi":"10.1145/3183399.3183411","DOIUrl":"https://doi.org/10.1145/3183399.3183411","url":null,"abstract":"Recent research provides evidence that effective communication in collaborative software development has significant impact on the software development lifecycle. Although related qualitative and quantitative studies point out textual characteristics of well-formed messages, the underlying semantics of the intertwined linguistic structures still remain largely misinterpreted or ignored. Especially, regarding quality of code reviews the importance of thorough feedback, and explicit rationale is often mentioned but rarely linked with related linguistic features. As a first step towards addressing this shortcoming, we propose grounding these studies on theories of linguistics. We particularly focus on linguistic structures of coherent speech and explain how they can be exploited in practice. We reflect on related approaches and examine through a preliminary study on four open source projects, possible links between existing findings and the directions we suggest for detecting textual features of useful code reviews.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129157304","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}
Context: There is growing interest in establishing software engineering as an evidence-based discipline. To that end, replication is often used to gain confidence in empirical findings, as opposed to reproduction where the goal is showing the correctness, or validity of the published results. Objective: To consider what is required for a replication study to confirm the original experiment and apply this understanding in software engineering. Method: Simulation is used to demonstrate why the prediction interval for confirmation can be surprisingly wide. This analysis is applied to three recent replications. Results: It is shown that because the prediction intervals are wide, almost all replications are confirmatory, so in that sense there is no 'replication crisis', however, the contributions to knowledge are negligible. Conclusion: Replicating empirical software engineering experiments, particularly if they are under-powered or under-reported, is a waste of scientific resources. By contrast, meta-analysis is strongly advocated so that all relevant experiments are combined to estimate the population effect.
{"title":"Replication Studies Considered Harmful","authors":"M. Shepperd","doi":"10.1145/3183399.3183423","DOIUrl":"https://doi.org/10.1145/3183399.3183423","url":null,"abstract":"Context: There is growing interest in establishing software engineering as an evidence-based discipline. To that end, replication is often used to gain confidence in empirical findings, as opposed to reproduction where the goal is showing the correctness, or validity of the published results. Objective: To consider what is required for a replication study to confirm the original experiment and apply this understanding in software engineering. Method: Simulation is used to demonstrate why the prediction interval for confirmation can be surprisingly wide. This analysis is applied to three recent replications. Results: It is shown that because the prediction intervals are wide, almost all replications are confirmatory, so in that sense there is no 'replication crisis', however, the contributions to knowledge are negligible. Conclusion: Replicating empirical software engineering experiments, particularly if they are under-powered or under-reported, is a waste of scientific resources. By contrast, meta-analysis is strongly advocated so that all relevant experiments are combined to estimate the population effect.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123165571","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 introduces the idea of mining container image repositories for configuration and other deployment information of software systems. Unlike traditional software repositories (e.g., source code repositories and app stores), image repositories encapsulate the entire execution ecosystem for running target software, including its configurations, dependent libraries and components, and OS-level utilities, which contributes to a wealth of data and information. We showcase the opportunities based on concrete software engineering tasks that can benefit from mining image repositories. To facilitate future mining efforts, we summarize the challenges of analyzing image repositories and the approaches that can address these challenges. We hope that this paper will stimulate exciting research agenda of mining this emerging type of software repositories.
{"title":"Mining Container Image Repositories for Software Configuration and Beyond","authors":"Tianyin Xu, D. Marinov","doi":"10.1145/3183399.3183403","DOIUrl":"https://doi.org/10.1145/3183399.3183403","url":null,"abstract":"This paper introduces the idea of mining container image repositories for configuration and other deployment information of software systems. Unlike traditional software repositories (e.g., source code repositories and app stores), image repositories encapsulate the entire execution ecosystem for running target software, including its configurations, dependent libraries and components, and OS-level utilities, which contributes to a wealth of data and information. We showcase the opportunities based on concrete software engineering tasks that can benefit from mining image repositories. To facilitate future mining efforts, we summarize the challenges of analyzing image repositories and the approaches that can address these challenges. We hope that this paper will stimulate exciting research agenda of mining this emerging type of software repositories.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133472555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software analytics has been the subject of considerable recent attention but is yet to receive significant industry traction. One of the key reasons is that software practitioners are reluctant to trust predictions produced by the analytics machinery without understanding the rationale for those predictions. While complex models such as deep learning and ensemble methods improve predictive performance, they have limited explainability. In this paper, we argue that making software analytics models explainable to software practitioners is as important as achieving accurate predictions. Explainability should therefore be a key measure for evaluating software analytics models. We envision that explainability will be a key driver for developing software analytics models that are useful in practice. We outline a research roadmap for this space, building on social science, explainable artificial intelligence and software engineering.
{"title":"Explainable Software Analytics","authors":"K. Dam, T. Tran, A. Ghose","doi":"10.1145/3183399.3183424","DOIUrl":"https://doi.org/10.1145/3183399.3183424","url":null,"abstract":"Software analytics has been the subject of considerable recent attention but is yet to receive significant industry traction. One of the key reasons is that software practitioners are reluctant to trust predictions produced by the analytics machinery without understanding the rationale for those predictions. While complex models such as deep learning and ensemble methods improve predictive performance, they have limited explainability. In this paper, we argue that making software analytics models explainable to software practitioners is as important as achieving accurate predictions. Explainability should therefore be a key measure for evaluating software analytics models. We envision that explainability will be a key driver for developing software analytics models that are useful in practice. We outline a research roadmap for this space, building on social science, explainable artificial intelligence and software engineering.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"337 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117004920","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}
Static code analysis is a powerful approach to detect quality deficiencies such as performance bottlenecks, safety violations or security vulnerabilities already during a software system's implementation. Yet, as current software systems continue to grow, current static-analysis systems more frequently face the problem of insufficient scalability. We argue that this is mainly due to the fact that current static analyses are implemented fully manually, often in general-purpose programming languages such as Java or C, or in declarative languages such as Datalog. This design choice predefines the way in which the static analysis evaluates, and limits the optimizations and extensions static-analysis designers can apply. To boost scalability to a new level, we propose to fuse static-analysis with just-in-time-optimization technology, introducing for the first time static analyses that are managed and inherently self-adaptive. Those analyses automatically adapt themselves to yield a performance/precision tradeoff that is optimal with respect to the analyzed software system and to the analysis itself. Self-adaptivity is enabled by the novel idea of designing a dedicated intermediate representation, not for the analyzed program but for the analysis itself. This representation allows for an automatic optimization and adaptation of the analysis code, both ahead-of-time (through static analysis of the static analysis) as well as just-in-time during the analysis' execution, similar to just-in-time compilers.
{"title":"Self-Adaptive Static Analysis","authors":"E. Bodden","doi":"10.1145/3183399.3183401","DOIUrl":"https://doi.org/10.1145/3183399.3183401","url":null,"abstract":"Static code analysis is a powerful approach to detect quality deficiencies such as performance bottlenecks, safety violations or security vulnerabilities already during a software system's implementation. Yet, as current software systems continue to grow, current static-analysis systems more frequently face the problem of insufficient scalability. We argue that this is mainly due to the fact that current static analyses are implemented fully manually, often in general-purpose programming languages such as Java or C, or in declarative languages such as Datalog. This design choice predefines the way in which the static analysis evaluates, and limits the optimizations and extensions static-analysis designers can apply. To boost scalability to a new level, we propose to fuse static-analysis with just-in-time-optimization technology, introducing for the first time static analyses that are managed and inherently self-adaptive. Those analyses automatically adapt themselves to yield a performance/precision tradeoff that is optimal with respect to the analyzed software system and to the analysis itself. Self-adaptivity is enabled by the novel idea of designing a dedicated intermediate representation, not for the analyzed program but for the analysis itself. This representation allows for an automatic optimization and adaptation of the analysis code, both ahead-of-time (through static analysis of the static analysis) as well as just-in-time during the analysis' execution, similar to just-in-time compilers.","PeriodicalId":212579,"journal":{"name":"2018 IEEE/ACM 40th International Conference on Software Engineering: New Ideas and Emerging Technologies Results (ICSE-NIER)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126694124","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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