G. Horváth, Péter Szécsi, Zoltán Gera, Dániel Krupp, Norbert Pataki
Static analysis is a great approach to find bugs and code smells. Some of the errors span across multiple translation units. Unfortunately, separate compilation makes cross translation unit analysis challenging for C family languages. In this paper, we describe a model and an implementation for cross translation unit symbolic execution for C family languages. We were able to extend the scope of the analysis without modifying any of the existing checkers. The analysis is implemented in the open source Clang compiler. We also measured the performance of the approach and the quality of the reports. The solution proved to be scalable to large codebases and the number of findings increased significantly for the evaluated projects. The implementation is already accepted into mainline Clang.
{"title":"[Engineering Paper] Challenges of Implementing Cross Translation Unit Analysis in Clang Static Analyzer","authors":"G. Horváth, Péter Szécsi, Zoltán Gera, Dániel Krupp, Norbert Pataki","doi":"10.1109/SCAM.2018.00027","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00027","url":null,"abstract":"Static analysis is a great approach to find bugs and code smells. Some of the errors span across multiple translation units. Unfortunately, separate compilation makes cross translation unit analysis challenging for C family languages. In this paper, we describe a model and an implementation for cross translation unit symbolic execution for C family languages. We were able to extend the scope of the analysis without modifying any of the existing checkers. The analysis is implemented in the open source Clang compiler. We also measured the performance of the approach and the quality of the reports. The solution proved to be scalable to large codebases and the number of findings increased significantly for the evaluated projects. The implementation is already accepted into mainline Clang.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532325","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}
David C. Shepherd, Patrick Francis, David Weintrop, Diana Franklin, Boyang Li, Afsoon Afzal
Many robotic tasks in small manufacturing sites are quite simple. For example, a pick and place task requires only a few common commands. Unfortunately, the standard languages and programming environments for industrial robots are complex, making even these simple tasks nearly impossible for novices. To enable novices to program simple tasks we created a block-based programming language and environment focused on usability, learnability, and understandability and embedded its programming environment in a state-of-the-art robot simulator. By using this high-fidelity prototype over the course of a year in a case study, a user study, and for countless demonstrations we have gained many concrete insights. In this paper we discuss the details of the language, the design of its programming environment, and concrete insights gained via longitudinal usage.
{"title":"[Engineering Paper] An IDE for Easy Programming of Simple Robotics Tasks","authors":"David C. Shepherd, Patrick Francis, David Weintrop, Diana Franklin, Boyang Li, Afsoon Afzal","doi":"10.1109/SCAM.2018.00032","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00032","url":null,"abstract":"Many robotic tasks in small manufacturing sites are quite simple. For example, a pick and place task requires only a few common commands. Unfortunately, the standard languages and programming environments for industrial robots are complex, making even these simple tasks nearly impossible for novices. To enable novices to program simple tasks we created a block-based programming language and environment focused on usability, learnability, and understandability and embedded its programming environment in a state-of-the-art robot simulator. By using this high-fidelity prototype over the course of a year in a case study, a user study, and for countless demonstrations we have gained many concrete insights. In this paper we discuss the details of the language, the design of its programming environment, and concrete insights gained via longitudinal usage.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133367734","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}
Pointer aliasing still hinders compiler optimizations. The ISO C standard 99 has added the restrict keyword that allows programmer to specify non-aliasing as an aid to the compiler's optimizer. The task of annotating pointers with the restrict keyword is still left to the programmer and this task is, in general, tedious and prone to errors. Scalar replacement is an optimization widely used by compilers. In this paper, we present two new Frama-C plug-ins, RECKA for automatic annotation of CUDA kernels arguments with the restrict keyword, and RPromF for scalar replacement in OpenACC and OpenMP 4.0/4.5 codes for GPU. More specifically, RECKA works as follows: (i) an alias analysis is performed on CUDA kernels and their callers; (ii) if not found any alias then CUDA kernels are cloned, the clones are renamed and their arguments are annotated with the restrict qualifier; and (iii) instructions are added to kernels call sites to perform at runtime a less-than check analysis on kernel actuals parameters and determine if the clone must be called or the original one. RPromF includes five main steps: (i) OpenACC/OpenMP offloading regions are identified; (ii) functions containing these offloading codes and their callers are analyzed to check that there is no alias; (iii) if there is no alias then the offloading codes are cloned; (iv) clone's instructions are analyzed to retrieve data reuse information and perform scalar replacement; and instructions are added to be able to use the optimized clone whenever possible. We have evaluated the two plug-ins on PolyBench benchmark suite. The results show that both scalar replacement and the usage of restrict keyword are effective for improving the overall performance of OpenACC, OpenMP 4.0/4.5 and CUDA codes.
{"title":"[Engineering Paper] RECKA and RPromF: Two Frama-C Plug-ins for Optimizing Registers Usage in CUDA, OpenACC and OpenMP Programs","authors":"R. Diarra, A. Mérigot, B. Vincke","doi":"10.1109/SCAM.2018.00029","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00029","url":null,"abstract":"Pointer aliasing still hinders compiler optimizations. The ISO C standard 99 has added the restrict keyword that allows programmer to specify non-aliasing as an aid to the compiler's optimizer. The task of annotating pointers with the restrict keyword is still left to the programmer and this task is, in general, tedious and prone to errors. Scalar replacement is an optimization widely used by compilers. In this paper, we present two new Frama-C plug-ins, RECKA for automatic annotation of CUDA kernels arguments with the restrict keyword, and RPromF for scalar replacement in OpenACC and OpenMP 4.0/4.5 codes for GPU. More specifically, RECKA works as follows: (i) an alias analysis is performed on CUDA kernels and their callers; (ii) if not found any alias then CUDA kernels are cloned, the clones are renamed and their arguments are annotated with the restrict qualifier; and (iii) instructions are added to kernels call sites to perform at runtime a less-than check analysis on kernel actuals parameters and determine if the clone must be called or the original one. RPromF includes five main steps: (i) OpenACC/OpenMP offloading regions are identified; (ii) functions containing these offloading codes and their callers are analyzed to check that there is no alias; (iii) if there is no alias then the offloading codes are cloned; (iv) clone's instructions are analyzed to retrieve data reuse information and perform scalar replacement; and instructions are added to be able to use the optimized clone whenever possible. We have evaluated the two plug-ins on PolyBench benchmark suite. The results show that both scalar replacement and the usage of restrict keyword are effective for improving the overall performance of OpenACC, OpenMP 4.0/4.5 and CUDA codes.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132160916","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}
As the quality and quantity of open source code increase, semantics-based code search has become an emerging need for software developers to retrieve and reuse existing source code. We present an approach of semantics-based code search using input/output examples for the Java language. Our approach encodes Java methods in code repositories into path constraints via symbolic analysis and leverages SMT solvers to find the methods whose path constraints can satisfy the given input/output examples. Our approach extends the applicability of the semantics-based search technology to more general Java code compared with existing methods. To evaluate our approach, we encoded 1228 methods from GitHub and applied semantics-based code search on 35 queries extracted from Stack Overflow. Correct method code for 29 queries was obtained during the search and the average search time was just about 48 seconds.
{"title":"[Research Paper] Semantics-Based Code Search Using Input/Output Examples","authors":"Renhe Jiang, Zhengzhao Chen, Zejun Zhang, Yu Pei, Minxue Pan, Tian Zhang","doi":"10.1109/SCAM.2018.00018","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00018","url":null,"abstract":"As the quality and quantity of open source code increase, semantics-based code search has become an emerging need for software developers to retrieve and reuse existing source code. We present an approach of semantics-based code search using input/output examples for the Java language. Our approach encodes Java methods in code repositories into path constraints via symbolic analysis and leverages SMT solvers to find the methods whose path constraints can satisfy the given input/output examples. Our approach extends the applicability of the semantics-based search technology to more general Java code compared with existing methods. To evaluate our approach, we encoded 1228 methods from GitHub and applied semantics-based code search on 35 queries extracted from Stack Overflow. Correct method code for 29 queries was obtained during the search and the average search time was just about 48 seconds.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126271845","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}
Md. Anaytul Islam, Md. Moksedul Islam, Manishankar Mondal, B. Roy, C. Roy, Kevin A. Schneider
If two or more program entities (such as files, classes, methods) co-change (i.e., change together) frequently during software evolution, then it is likely that these two entities are coupled (i.e., the entities are related). Such a coupling is termed as evolutionary coupling in the literature. The concept of traditional evolutionary coupling restricts us to assume coupling among only those entities that changed together in the past. The entities that did not co-change in the past might also have coupling. However, such couplings can not be retrieved using the current concept of detecting evolutionary coupling in the literature. In this paper, we investigate whether we can detect such couplings by applying transitive rules on the evolutionary couplings detected using the traditional mechanism. We call these couplings that we detect using our proposed mechanism as transitive evolutionary couplings. According to our research on thousands of revisions of four subject systems, transitive evolutionary couplings combined with the traditional ones provide us with 13.96% higher recall and 5.56% higher precision in detecting future co-change candidates when compared with a state-of-the-art technique.
{"title":"[Research Paper] Detecting Evolutionary Coupling Using Transitive Association Rules","authors":"Md. Anaytul Islam, Md. Moksedul Islam, Manishankar Mondal, B. Roy, C. Roy, Kevin A. Schneider","doi":"10.1109/SCAM.2018.00020","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00020","url":null,"abstract":"If two or more program entities (such as files, classes, methods) co-change (i.e., change together) frequently during software evolution, then it is likely that these two entities are coupled (i.e., the entities are related). Such a coupling is termed as evolutionary coupling in the literature. The concept of traditional evolutionary coupling restricts us to assume coupling among only those entities that changed together in the past. The entities that did not co-change in the past might also have coupling. However, such couplings can not be retrieved using the current concept of detecting evolutionary coupling in the literature. In this paper, we investigate whether we can detect such couplings by applying transitive rules on the evolutionary couplings detected using the traditional mechanism. We call these couplings that we detect using our proposed mechanism as transitive evolutionary couplings. According to our research on thousands of revisions of four subject systems, transitive evolutionary couplings combined with the traditional ones provide us with 13.96% higher recall and 5.56% higher precision in detecting future co-change candidates when compared with a state-of-the-art technique.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126571175","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}
{"title":"Title Page i","authors":"","doi":"10.1109/scam.2018.00001","DOIUrl":"https://doi.org/10.1109/scam.2018.00001","url":null,"abstract":"","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128692535","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 often practice re-use by copying and pasting code. Copied and pasted code is also known as clones. Clones may be found in all programming languages. Automated clone detection may help to detect clones in order to support software maintenance and language design. Syntax-based clone detectors find similar syntax subtrees and, hence, are guaranteed to yield only syntactic clones. They are also known to have high precision and good recall. Developing a syntax-based clone detector for each language from scratch may be an expensive task. In this paper, we explore the idea to integrate syntax-based clone detection into workbenches for language engineering. Such workbenches allow developers to create their own domain-specific language or to create parsers for existing languages. With the integration of clone detection into these workbenches, a clone detector comes as a free byproduct of the grammar specification. The effort is spent only once for the workbench and not multiple times for every language built with the workbench. We report our lessons learned in applying this idea for three language workbenches: the popular parser generator ANTLR and two language workbenches for domain-specific languages, namely, MPS, developed by JetBrains, and Xtext, which is based on the Eclipse Modeling Framework.
{"title":"[Engineering Paper] Built-in Clone Detection in Meta Languages","authors":"R. Koschke, Urs-Bjorn Schmidt, Bernhard J. Berger","doi":"10.1109/SCAM.2018.00026","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00026","url":null,"abstract":"Developers often practice re-use by copying and pasting code. Copied and pasted code is also known as clones. Clones may be found in all programming languages. Automated clone detection may help to detect clones in order to support software maintenance and language design. Syntax-based clone detectors find similar syntax subtrees and, hence, are guaranteed to yield only syntactic clones. They are also known to have high precision and good recall. Developing a syntax-based clone detector for each language from scratch may be an expensive task. In this paper, we explore the idea to integrate syntax-based clone detection into workbenches for language engineering. Such workbenches allow developers to create their own domain-specific language or to create parsers for existing languages. With the integration of clone detection into these workbenches, a clone detector comes as a free byproduct of the grammar specification. The effort is spent only once for the workbench and not multiple times for every language built with the workbench. We report our lessons learned in applying this idea for three language workbenches: the popular parser generator ANTLR and two language workbenches for domain-specific languages, namely, MPS, developed by JetBrains, and Xtext, which is based on the Eclipse Modeling Framework.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114832072","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}
Kawser Wazed Nafi, B. Roy, C. Roy, Kevin A. Schneider
In today's open source era, developers look forsimilar software applications in source code repositories for anumber of reasons, including, exploring alternative implementations, reusing source code, or looking for a better application. However, while there are a great many studies for finding similarapplications written in the same programming language, there isa marked lack of studies for finding similar software applicationswritten in different languages. In this paper, we fill the gapby proposing a novel modelCroLSimwhich is able to detectsimilar software applications across different programming lan-guages. In our approach, we use the API documentation tofind relationships among the API calls used by the differentprogramming languages. We adopt a deep learning based word-vector learning method to identify semantic relationships amongthe API documentation which we then use to detect cross-language similar software applications. For evaluating CroLSim, we formed a repository consisting of 8,956 Java, 7,658 C#, and 10,232 Python applications collected from GitHub. Weobserved thatCroLSimcan successfully detect similar softwareapplications across different programming languages with a meanaverage precision rate of 0.65, an average confidence rate of3.6 (out of 5) with 75% high rated successful queries, whichoutperforms all related existing approaches with a significantperformance improvement.
{"title":"[Research Paper] CroLSim: Cross Language Software Similarity Detector Using API Documentation","authors":"Kawser Wazed Nafi, B. Roy, C. Roy, Kevin A. Schneider","doi":"10.1109/SCAM.2018.00023","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00023","url":null,"abstract":"In today's open source era, developers look forsimilar software applications in source code repositories for anumber of reasons, including, exploring alternative implementations, reusing source code, or looking for a better application. However, while there are a great many studies for finding similarapplications written in the same programming language, there isa marked lack of studies for finding similar software applicationswritten in different languages. In this paper, we fill the gapby proposing a novel modelCroLSimwhich is able to detectsimilar software applications across different programming lan-guages. In our approach, we use the API documentation tofind relationships among the API calls used by the differentprogramming languages. We adopt a deep learning based word-vector learning method to identify semantic relationships amongthe API documentation which we then use to detect cross-language similar software applications. For evaluating CroLSim, we formed a repository consisting of 8,956 Java, 7,658 C#, and 10,232 Python applications collected from GitHub. Weobserved thatCroLSimcan successfully detect similar softwareapplications across different programming languages with a meanaverage precision rate of 0.65, an average confidence rate of3.6 (out of 5) with 75% high rated successful queries, whichoutperforms all related existing approaches with a significantperformance improvement.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124842720","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}
Studies on security vulnerabilities require the analysis, investigation and comprehension of real vulnerable code instances. However, collecting and experimenting with a sufficient number of such instances is challenging. To cope with this issue, we developed VulData7, an extensible framework and dataset of real vulnerabilities, automatically collected from software archives. The current version of the dataset contains all reported vulnerabilities (in the NVD database) of 4 security critical open source systems, i.e., Linux Kernel, WireShark, OpenSSL, SystemD. For each vulnerability, VulData7 provides the vulnerability report data (description, CVE number, CWE number, CVSS severity score and others), the vulnerable code instance (list of versions), and when available its corresponding patches (list of fixing commits) and the files (before and after fix). VulData7 is automated, flexible and easily extensible. Once configured, it extracts and links information from the related software archives (through Git and NVD reports) to create a dataset that is continuously updated with the latest information available. Currently, VulData7 retrieves fixes for 1,600 out of the 2,800 reported vulnerabilities of the 4 systems. The framework also supports the collection of additional software defects and aims at easing empirical studies and analyses. We believe that our framework is a valuable resource for both developers and researchers interested in secure software development. Vul-Data7 can also serve educational purposes and trigger research on source code analysis. VulData7 is publicly available at: https://github.com/electricalwind/data7
{"title":"[Engineering Paper] Enabling the Continuous Analysis of Security Vulnerabilities with VulData7","authors":"Matthieu Jimenez, Yves Le Traon, Mike Papadakis","doi":"10.1109/SCAM.2018.00014","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00014","url":null,"abstract":"Studies on security vulnerabilities require the analysis, investigation and comprehension of real vulnerable code instances. However, collecting and experimenting with a sufficient number of such instances is challenging. To cope with this issue, we developed VulData7, an extensible framework and dataset of real vulnerabilities, automatically collected from software archives. The current version of the dataset contains all reported vulnerabilities (in the NVD database) of 4 security critical open source systems, i.e., Linux Kernel, WireShark, OpenSSL, SystemD. For each vulnerability, VulData7 provides the vulnerability report data (description, CVE number, CWE number, CVSS severity score and others), the vulnerable code instance (list of versions), and when available its corresponding patches (list of fixing commits) and the files (before and after fix). VulData7 is automated, flexible and easily extensible. Once configured, it extracts and links information from the related software archives (through Git and NVD reports) to create a dataset that is continuously updated with the latest information available. Currently, VulData7 retrieves fixes for 1,600 out of the 2,800 reported vulnerabilities of the 4 systems. The framework also supports the collection of additional software defects and aims at easing empirical studies and analyses. We believe that our framework is a valuable resource for both developers and researchers interested in secure software development. Vul-Data7 can also serve educational purposes and trigger research on source code analysis. VulData7 is publicly available at: https://github.com/electricalwind/data7","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131039831","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 describes some of the challenges and opportunities when deploying static and dynamic analysis at scale, drawing on the authors' experience with the Infer and Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, impacting billions of people worldwide. The paper identifies open problems that have yet to receive significant attention from the scientific community, yet which have potential for profound real world impact, formulating these as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. Note: This paper accompanies the authors' joint keynote at the 18th IEEE International Working Conference on Source Code Analysis and Manipulation, September 23rd-24th, 2018 - Madrid, Spain.
{"title":"From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis","authors":"M. Harman, P. O'Hearn","doi":"10.1109/SCAM.2018.00009","DOIUrl":"https://doi.org/10.1109/SCAM.2018.00009","url":null,"abstract":"This paper describes some of the challenges and opportunities when deploying static and dynamic analysis at scale, drawing on the authors' experience with the Infer and Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, impacting billions of people worldwide. The paper identifies open problems that have yet to receive significant attention from the scientific community, yet which have potential for profound real world impact, formulating these as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. Note: This paper accompanies the authors' joint keynote at the 18th IEEE International Working Conference on Source Code Analysis and Manipulation, September 23rd-24th, 2018 - Madrid, Spain.","PeriodicalId":127335,"journal":{"name":"2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574807","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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