Titus Barik, Rahul Pandita, Justin Middleton, E. Murphy-Hill
Software engineering researchers have a tendency to be optimistic about the future. Though useful, optimism bias bolsters unrealistic expectations towards desirable outcomes. We argue that explicitly framing software engineering research through pessimistic futures, or dystopias, will mitigate optimism bias and engender more diverse and thought-provoking research directions. We demonstrate through three pop culture dystopias, Battlestar Galactica, Fallout 3, and Children of Men, how reflecting on dystopian scenarios provides research opportunities as well as implications, such as making research accessible to non-experts, that are relevant to our present.
{"title":"Designing for dystopia: software engineering research for the post-apocalypse","authors":"Titus Barik, Rahul Pandita, Justin Middleton, E. Murphy-Hill","doi":"10.1145/2950290.2983986","DOIUrl":"https://doi.org/10.1145/2950290.2983986","url":null,"abstract":"Software engineering researchers have a tendency to be optimistic about the future. Though useful, optimism bias bolsters unrealistic expectations towards desirable outcomes. We argue that explicitly framing software engineering research through pessimistic futures, or dystopias, will mitigate optimism bias and engender more diverse and thought-provoking research directions. We demonstrate through three pop culture dystopias, Battlestar Galactica, Fallout 3, and Children of Men, how reflecting on dystopian scenarios provides research opportunities as well as implications, such as making research accessible to non-experts, that are relevant to our present.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75479991","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}
We present ECHO, a new technique that detects data races instantaneously in the IDE while developers code. ECHO is the first technique of its kind for incremental race detection supporting both code addition and deletion in the IDE. Unlike conventional static race detectors, ECHO warns developers of potential data races immediately as they are introduced into the program. The core underpinning ECHO is a set of new change-aware static analyses based on a novel static happens-before graph that, given a program change, efficiently compute the change-relevant information without re-analyzing the whole program. Our evaluation within a Java environment on both popular benchmarks and real- world applications shows promising results: for each code addition, or deletion, ECHO can instantly pinpoint all the races in a few milliseconds on average, three to four orders of magnitude faster than a conventional whole-program race detector with the same precision.
{"title":"ECHO: instantaneous in situ race detection in the IDE","authors":"Sheng Zhan, Jeff Huang","doi":"10.1145/2950290.2950332","DOIUrl":"https://doi.org/10.1145/2950290.2950332","url":null,"abstract":"We present ECHO, a new technique that detects data races instantaneously in the IDE while developers code. ECHO is the first technique of its kind for incremental race detection supporting both code addition and deletion in the IDE. Unlike conventional static race detectors, ECHO warns developers of potential data races immediately as they are introduced into the program. The core underpinning ECHO is a set of new change-aware static analyses based on a novel static happens-before graph that, given a program change, efficiently compute the change-relevant information without re-analyzing the whole program. Our evaluation within a Java environment on both popular benchmarks and real- world applications shows promising results: for each code addition, or deletion, ECHO can instantly pinpoint all the races in a few milliseconds on average, three to four orders of magnitude faster than a conventional whole-program race detector with the same precision.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85907519","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}
Redundant traversal of loops in the context of other loops has been recently identified as a source of performance bugs in many Java libraries. This has resulted in the design of static and dynamic analysis techniques to detect these performance bugs automatically. However, while the effectiveness of dynamic analyses is dependent on the analyzed input tests, static analyses are less effective in automatically validating the presence of these problems, validating the fixes and avoiding regressions in future versions. This necessitates the design of an approach to automatically generate tests for exposing redundant traversal of loops. In this paper, we design a novel, scalable and automatic approach that addresses this goal. Our approach takes a library and an initial set of coverage-driven randomly generated tests as input and generates tests which enable detection of redundant traversal of loops. Our approach is broadly composed of three phases – analysis of the execution of random tests to generate method summaries, identification of methods with potential nested loops along with the appropriate context to expose the problem, and test generation to invoke the identified methods with the appropriate parameters. The generated tests can be analyzed by existing dynamic tools to detect possible performance issues. We have implemented our approach on top of the SOOT bytecode analysis framework and validated it on many open-source Java libraries. Our experiments reveal the effectiveness of our approach in generating 224 tests that reveal 46 bugs across seven libraries, including 34 previously unknown bugs. The tests generated using our approach significantly outperform the randomly generated tests in their ability to expose the inefficiencies, demonstrating the usefulness of our design. The implementation of our tool, named Glider, is available at http://drona.csa.iisc.ac.in/~sss/tools/glider.
{"title":"Directed test generation to detect loop inefficiencies","authors":"Monika Dhok, M. Ramanathan","doi":"10.1145/2950290.2950360","DOIUrl":"https://doi.org/10.1145/2950290.2950360","url":null,"abstract":"Redundant traversal of loops in the context of other loops has been recently identified as a source of performance bugs in many Java libraries. This has resulted in the design of static and dynamic analysis techniques to detect these performance bugs automatically. However, while the effectiveness of dynamic analyses is dependent on the analyzed input tests, static analyses are less effective in automatically validating the presence of these problems, validating the fixes and avoiding regressions in future versions. This necessitates the design of an approach to automatically generate tests for exposing redundant traversal of loops. In this paper, we design a novel, scalable and automatic approach that addresses this goal. Our approach takes a library and an initial set of coverage-driven randomly generated tests as input and generates tests which enable detection of redundant traversal of loops. Our approach is broadly composed of three phases – analysis of the execution of random tests to generate method summaries, identification of methods with potential nested loops along with the appropriate context to expose the problem, and test generation to invoke the identified methods with the appropriate parameters. The generated tests can be analyzed by existing dynamic tools to detect possible performance issues. We have implemented our approach on top of the SOOT bytecode analysis framework and validated it on many open-source Java libraries. Our experiments reveal the effectiveness of our approach in generating 224 tests that reveal 46 bugs across seven libraries, including 34 previously unknown bugs. The tests generated using our approach significantly outperform the randomly generated tests in their ability to expose the inefficiencies, demonstrating the usefulness of our design. The implementation of our tool, named Glider, is available at http://drona.csa.iisc.ac.in/~sss/tools/glider.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86888877","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}
Iftekhar Ahmed, Rahul Gopinath, Caius Brindescu, Alex Groce, Carlos Jensen
Among the major questions that a practicing tester faces are deciding where to focus additional testing effort, and deciding when to stop testing. Test the least-tested code, and stop when all code is well-tested, is a reasonable answer. Many measures of "testedness" have been proposed; unfortunately, we do not know whether these are truly effective. In this paper we propose a novel evaluation of two of the most important and widely-used measures of test suite quality. The first measure is statement coverage, the simplest and best-known code coverage measure. The second measure is mutation score, a supposedly more powerful, though expensive, measure. We evaluate these measures using the actual criteria of interest: if a program element is (by these measures) well tested at a given point in time, it should require fewer future bug-fixes than a "poorly tested" element. If not, then it seems likely that we are not effectively measuring testedness. Using a large number of open source Java programs from Github and Apache, we show that both statement coverage and mutation score have only a weak negative correlation with bug-fixes. Despite the lack of strong correlation, there are statistically and practically significant differences between program elements for various binary criteria. Program elements (other than classes) covered by any test case see about half as many bug-fixes as those not covered, and a similar line can be drawn for mutation score thresholds. Our results have important implications for both software engineering practice and research evaluation.
{"title":"Can testedness be effectively measured?","authors":"Iftekhar Ahmed, Rahul Gopinath, Caius Brindescu, Alex Groce, Carlos Jensen","doi":"10.1145/2950290.2950324","DOIUrl":"https://doi.org/10.1145/2950290.2950324","url":null,"abstract":"Among the major questions that a practicing tester faces are deciding where to focus additional testing effort, and deciding when to stop testing. Test the least-tested code, and stop when all code is well-tested, is a reasonable answer. Many measures of \"testedness\" have been proposed; unfortunately, we do not know whether these are truly effective. In this paper we propose a novel evaluation of two of the most important and widely-used measures of test suite quality. The first measure is statement coverage, the simplest and best-known code coverage measure. The second measure is mutation score, a supposedly more powerful, though expensive, measure. We evaluate these measures using the actual criteria of interest: if a program element is (by these measures) well tested at a given point in time, it should require fewer future bug-fixes than a \"poorly tested\" element. If not, then it seems likely that we are not effectively measuring testedness. Using a large number of open source Java programs from Github and Apache, we show that both statement coverage and mutation score have only a weak negative correlation with bug-fixes. Despite the lack of strong correlation, there are statistically and practically significant differences between program elements for various binary criteria. Program elements (other than classes) covered by any test case see about half as many bug-fixes as those not covered, and a similar line can be drawn for mutation score thresholds. Our results have important implications for both software engineering practice and research evaluation.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90121109","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}
Performance bottlenecks related to a program's memory behavior are common, yet very hard to debug. Tools that attempt to aid software engineers in diagnosing these bugs are typically designed to handle specific use cases; they do not provide information to comprehensively explore memory problems and to find solutions. Detailed traces of memory accesses would enable developers to ask various questions about the program's memory behaviour, but these traces quickly become very large even for short executions. We present DINAMITE: a toolkit for Dynamic INstrumentation and Analysis for MassIve Trace Exploration. DINAMITE instruments every memory access with highly debug information and provides a suite of extensible analysis tools to aid programmers in pinpointing memory bottlenecks.
{"title":"End-to-end memory behavior profiling with DINAMITE","authors":"Svetozar Miucin, C. Brady, Alexandra Fedorova","doi":"10.1145/2950290.2983941","DOIUrl":"https://doi.org/10.1145/2950290.2983941","url":null,"abstract":"Performance bottlenecks related to a program's memory behavior are common, yet very hard to debug. Tools that attempt to aid software engineers in diagnosing these bugs are typically designed to handle specific use cases; they do not provide information to comprehensively explore memory problems and to find solutions. Detailed traces of memory accesses would enable developers to ask various questions about the program's memory behaviour, but these traces quickly become very large even for short executions. We present DINAMITE: a toolkit for Dynamic INstrumentation and Analysis for MassIve Trace Exploration. DINAMITE instruments every memory access with highly debug information and provides a suite of extensible analysis tools to aid programmers in pinpointing memory bottlenecks.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90394603","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}
Normative systems (i.e., a set of rules) are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given a minimal effective normative system which coordinates many agents must be minimal and effective for a small number of agents, we start with automatically synthesizing one such system with a few agents. We then increase the number of agents so as to check whether the same design remains minimal and effective. If it is, we manually establish an induction proof so as to lift the design to an arbitrary number of agents.
{"title":"Designing minimal effective normative systems with the help of lightweight formal methods","authors":"Jianye Hao, Eunsuk Kang, Jun Sun, D. Jackson","doi":"10.1145/2950290.2950307","DOIUrl":"https://doi.org/10.1145/2950290.2950307","url":null,"abstract":"Normative systems (i.e., a set of rules) are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given a minimal effective normative system which coordinates many agents must be minimal and effective for a small number of agents, we start with automatically synthesizing one such system with a few agents. We then increase the number of agents so as to check whether the same design remains minimal and effective. If it is, we manually establish an induction proof so as to lift the design to an arbitrary number of agents.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79224698","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}
Chuck Rossi, Elisa Shibley, Shi Su, Kent L. Beck, T. Savor, M. Stumm
Continuous deployment is the practice of releasing software updates to production as soon as it is ready, which is receiving increased adoption in industry. The frequency of updates of mobile software has traditionally lagged the state of practice for cloud-based services for a number of reasons. Mobile versions can only be released periodically. Users can choose when and if to upgrade, which means that several different releases coexist in production. There are hundreds of Android hardware variants, which increases the risk of having errors in the software being deployed. Facebook has made significant progress in increasing the frequency of its mobile deployments. Over a period of 4 years, the Android release has gone from a deployment every 8 weeks to a deployment every week. In this paper, we describe in detail the mobile deployment process at FB. We present our findings from an extensive analysis of software engineering metrics based on data collected over a period of 7 years. A key finding is that the frequency of deployment does not directly affect developer productivity or software quality. We argue that this finding is due to the fact that increasing the frequency of continuous deployment forces improved release and deployment automation, which in turn reduces developer workload. Additionally, the data we present shows that dog-fooding and obtaining feedback from alpha and beta customers is critical to maintaining release quality.
{"title":"Continuous deployment of mobile software at facebook (showcase)","authors":"Chuck Rossi, Elisa Shibley, Shi Su, Kent L. Beck, T. Savor, M. Stumm","doi":"10.1145/2950290.2994157","DOIUrl":"https://doi.org/10.1145/2950290.2994157","url":null,"abstract":"Continuous deployment is the practice of releasing software updates to production as soon as it is ready, which is receiving increased adoption in industry. The frequency of updates of mobile software has traditionally lagged the state of practice for cloud-based services for a number of reasons. Mobile versions can only be released periodically. Users can choose when and if to upgrade, which means that several different releases coexist in production. There are hundreds of Android hardware variants, which increases the risk of having errors in the software being deployed. Facebook has made significant progress in increasing the frequency of its mobile deployments. Over a period of 4 years, the Android release has gone from a deployment every 8 weeks to a deployment every week. In this paper, we describe in detail the mobile deployment process at FB. We present our findings from an extensive analysis of software engineering metrics based on data collected over a period of 7 years. A key finding is that the frequency of deployment does not directly affect developer productivity or software quality. We argue that this finding is due to the fact that increasing the frequency of continuous deployment forces improved release and deployment automation, which in turn reduces developer workload. Additionally, the data we present shows that dog-fooding and obtaining feedback from alpha and beta customers is critical to maintaining release quality.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79466135","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}
Many applications require specialized data structures not found in standard libraries. Implementing new data structures by hand is tedious and error-prone. To alleviate this difficulty, we built a tool called Cozy that synthesizes data structures using counter-example guided inductive synthesis. We evaluate Cozy by showing how its synthesized implementations compare to handwritten implementations in terms of correctness and performance across four real-world programs. Cozy's data structures match the performance of the handwritten implementations while avoiding human error.
{"title":"Cozy: synthesizing collection data structures","authors":"Calvin Loncaric","doi":"10.1145/2950290.2986032","DOIUrl":"https://doi.org/10.1145/2950290.2986032","url":null,"abstract":"Many applications require specialized data structures not found in standard libraries. Implementing new data structures by hand is tedious and error-prone. To alleviate this difficulty, we built a tool called Cozy that synthesizes data structures using counter-example guided inductive synthesis. We evaluate Cozy by showing how its synthesized implementations compare to handwritten implementations in terms of correctness and performance across four real-world programs. Cozy's data structures match the performance of the handwritten implementations while avoiding human error.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79635595","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 year, software development teams around the world are consuming BILLIONS of open source and third-party components. The good news: they are accelerating time to market. The bad news: 1 in 17 components they are using include known security vulnerabilities. In this talk, I will describe what Sonatype, the company behind The Central Repository that supports Apache Maven, has learned from analyzing how thousands of applications use open source components. I will also discuss how organizations like Mayo Clinic, Exxon, Capital One, the U.S. FDA and Intuit are utilizing the principles of software supply chain automation to improve application security and how organizations can balance the need for speed with quality and security early in the development cycle.
{"title":"Making invisible things visible: tracking down known vulnerabilities at 3000 companies (showcase)","authors":"Gazi Mahmud","doi":"10.1145/2950290.2994155","DOIUrl":"https://doi.org/10.1145/2950290.2994155","url":null,"abstract":"This year, software development teams around the world are consuming BILLIONS of open source and third-party components. The good news: they are accelerating time to market. The bad news: 1 in 17 components they are using include known security vulnerabilities. In this talk, I will describe what Sonatype, the company behind The Central Repository that supports Apache Maven, has learned from analyzing how thousands of applications use open source components. I will also discuss how organizations like Mayo Clinic, Exxon, Capital One, the U.S. FDA and Intuit are utilizing the principles of software supply chain automation to improve application security and how organizations can balance the need for speed with quality and security early in the development cycle.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82601195","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}
Michael Reif, Michael Eichberg, Ben Hermann, Johannes Lerch, M. Mezini
Today, every application uses software libraries. Yet, while a lot of research exists w.r.t. analyzing applications, research that targets the analysis of libraries independent of any application is scarce. This is unfortunate, because, for developers of libraries, such as the Java Development Kit (JDK), it is crucial to ensure that the library behaves as intended regardless of how it is used. To fill this gap, we discuss the construction of call graphs for libraries that abstract over all potential library usages. Call graphs are particularly relevant as they are a precursor of many advanced analyses, such as inter-procedural data-flow analyses. We show that the current practice of using call graph algorithms designed for applications to analyze libraries leads to call graphs that, at the same time, lack relevant call edges and contain unnecessary edges. This motivates the need for call graph construction algorithms dedicated to libraries. Unlike algorithms for applications, call graph construction algorithms for libraries must take into consideration the goals of subsequent analyses. Specifically, we show that it is essential to distinguish between the scenario of an analysis for potential exploitable vulnerabilities from the scenario of an analysis for general software quality attributes, e.g., dead methods or unused fields. This distinction affects the decision about what constitutes the library-private implementation, which therefore, needs special treatment. Thus, building one call graph that satisfies all needs is not sensical. Overall, we observed that the proposed call graph algorithms reduce the number of call edges up to 30% when compared to existing approaches.
今天，每个应用程序都使用软件库。然而，尽管存在大量的研究来分析应用程序，但是针对独立于任何应用程序的库分析的研究却很少。这是不幸的，因为对于库(如Java Development Kit (JDK))的开发人员来说，无论如何使用，确保库按照预期的方式运行是至关重要的。为了填补这一空白，我们讨论了抽象所有潜在库用法的库的调用图的构造。调用图特别重要，因为它们是许多高级分析的先驱，例如过程间数据流分析。我们表明，目前使用为应用程序设计的调用图算法来分析库的做法导致调用图同时缺乏相关的调用边并包含不必要的边。这激发了对专用于库的调用图构造算法的需求。与应用程序的算法不同，库的调用图构造算法必须考虑后续分析的目标。具体地说，我们表明有必要区分分析潜在可利用漏洞的场景和分析一般软件质量属性的场景，例如，失效方法或未使用的字段。这种区别影响到决定是什么构成了库私有实现，因此需要特殊处理。因此，构建一个满足所有需求的调用图是没有意义的。总的来说，我们观察到，与现有方法相比，所提出的调用图算法将调用边的数量减少了30%。
{"title":"Call graph construction for Java libraries","authors":"Michael Reif, Michael Eichberg, Ben Hermann, Johannes Lerch, M. Mezini","doi":"10.1145/2950290.2950312","DOIUrl":"https://doi.org/10.1145/2950290.2950312","url":null,"abstract":"Today, every application uses software libraries. Yet, while a lot of research exists w.r.t. analyzing applications, research that targets the analysis of libraries independent of any application is scarce. This is unfortunate, because, for developers of libraries, such as the Java Development Kit (JDK), it is crucial to ensure that the library behaves as intended regardless of how it is used. To fill this gap, we discuss the construction of call graphs for libraries that abstract over all potential library usages. Call graphs are particularly relevant as they are a precursor of many advanced analyses, such as inter-procedural data-flow analyses. We show that the current practice of using call graph algorithms designed for applications to analyze libraries leads to call graphs that, at the same time, lack relevant call edges and contain unnecessary edges. This motivates the need for call graph construction algorithms dedicated to libraries. Unlike algorithms for applications, call graph construction algorithms for libraries must take into consideration the goals of subsequent analyses. Specifically, we show that it is essential to distinguish between the scenario of an analysis for potential exploitable vulnerabilities from the scenario of an analysis for general software quality attributes, e.g., dead methods or unused fields. This distinction affects the decision about what constitutes the library-private implementation, which therefore, needs special treatment. Thus, building one call graph that satisfies all needs is not sensical. Overall, we observed that the proposed call graph algorithms reduce the number of call edges up to 30% when compared to existing approaches.","PeriodicalId":20532,"journal":{"name":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82647672","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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