Pub Date : 2017-10-30DOI: 10.1109/ASE.2017.8115675
Ripon K. Saha, Yingjun Lyu, H. Yoshida, M. Prasad
This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.
{"title":"Elixir: Effective object-oriented program repair","authors":"Ripon K. Saha, Yingjun Lyu, H. Yoshida, M. Prasad","doi":"10.1109/ASE.2017.8115675","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115675","url":null,"abstract":"This work is motivated by the pervasive use of method invocations in object-oriented (OO) programs, and indeed their prevalence in patches of OO-program bugs. We propose a generate-and-validate repair technique, called ELIXIR designed to be able to generate such patches. ELIXIR aggressively uses method calls, on par with local variables, fields, or constants, to construct more expressive repair-expressions, that go into synthesizing patches. The ensuing enlargement of the repair space, on account of the wider use of method calls, is effectively tackled by using a machine-learnt model to rank concrete repairs. The machine-learnt model relies on four features derived from the program context, i.e., the code surrounding the potential repair location, and the bug report. We implement ELIXIR and evaluate it on two datasets, the popular Defects4J dataset and a new dataset Bugs.jar created by us, and against 2 baseline versions of our technique, and 5 other techniques representing the state of the art in program repair. Our evaluation shows that ELIXIR is able to increase the number of correctly repaired bugs in Defects4J by 85% (from 14 to 26) and by 57% in Bugs.jar (from 14 to 22), while also significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133933757","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115654
Simone Scalabrino, G. Bavota, Christopher Vendome, M. Vásquez, D. Poshyvanyk, R. Oliveto
Program understanding plays a pivotal role in software maintenance and evolution: a deep understanding of code is the stepping stone for most software-related activities, such as bug fixing or testing. Being able to measure the understandability of a piece of code might help in estimating the effort required for a maintenance activity, in comparing the quality of alternative implementations, or even in predicting bugs. Unfortunately, there are no existing metrics specifically designed to assess the understandability of a given code snippet. In this paper, we perform a first step in this direction, by studying the extent to which several types of metrics computed on code, documentation, and developers correlate with code understandability. To perform such an investigation we ran a study with 46 participants who were asked to understand eight code snippets each. We collected a total of 324 evaluations aiming at assessing the perceived understandability, the actual level of understanding, and the time needed to understand a code snippet. Our results demonstrate that none of the (existing and new) metrics we considered is able to capture code understandability, not even the ones assumed to assess quality attributes strongly related with it, such as code readability and complexity.
{"title":"Automatically assessing code understandability: How far are we?","authors":"Simone Scalabrino, G. Bavota, Christopher Vendome, M. Vásquez, D. Poshyvanyk, R. Oliveto","doi":"10.1109/ASE.2017.8115654","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115654","url":null,"abstract":"Program understanding plays a pivotal role in software maintenance and evolution: a deep understanding of code is the stepping stone for most software-related activities, such as bug fixing or testing. Being able to measure the understandability of a piece of code might help in estimating the effort required for a maintenance activity, in comparing the quality of alternative implementations, or even in predicting bugs. Unfortunately, there are no existing metrics specifically designed to assess the understandability of a given code snippet. In this paper, we perform a first step in this direction, by studying the extent to which several types of metrics computed on code, documentation, and developers correlate with code understandability. To perform such an investigation we ran a study with 46 participants who were asked to understand eight code snippets each. We collected a total of 324 evaluations aiming at assessing the perceived understandability, the actual level of understanding, and the time needed to understand a code snippet. Our results demonstrate that none of the (existing and new) metrics we considered is able to capture code understandability, not even the ones assumed to assess quality attributes strongly related with it, such as code readability and complexity.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132883731","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115640
Liang He, Yan Cai, Hong Hu, Purui Su, Zhenkai Liang, Yi Yang, Huafeng Huang, Jia Yan, Xiangkun Jia, D. Feng
Heap overflow is one of the most widely exploited vulnerabilities, with a large number of heap overflow instances reported every year. It is important to decide whether a crash caused by heap overflow can be turned into an exploit. Efficient and effective assessment of exploitability of crashes facilitates to identify severe vulnerabilities and thus prioritize resources. In this paper, we propose the first metrics to assess heap overflow crashes based on both the attack aspect and the feasibility aspect. We further present HCSIFTER, a novel solution to automatically assess the exploitability of heap overflow instances under our metrics. Given a heap-based crash, HCSIFTER accurately detects heap overflows through dynamic execution without any source code or debugging information. Then it uses several novel methods to extract program execution information needed to quantify the severity of the heap overflow using our metrics. We have implemented a prototype HCSIFTER and applied it to assess nine programs with heap overflow vulnerabilities. HCSIFTER successfully reports that five heap overflow vulnerabilities are highly exploitable and two overflow vulnerabilities are unlikely exploitable. It also gave quantitatively assessments for other two programs. On average, it only takes about two minutes to assess one heap overflow crash. The evaluation result demonstrates both effectiveness and efficiency of HC Sifter.
{"title":"Automatically assessing crashes from heap overflows","authors":"Liang He, Yan Cai, Hong Hu, Purui Su, Zhenkai Liang, Yi Yang, Huafeng Huang, Jia Yan, Xiangkun Jia, D. Feng","doi":"10.1109/ASE.2017.8115640","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115640","url":null,"abstract":"Heap overflow is one of the most widely exploited vulnerabilities, with a large number of heap overflow instances reported every year. It is important to decide whether a crash caused by heap overflow can be turned into an exploit. Efficient and effective assessment of exploitability of crashes facilitates to identify severe vulnerabilities and thus prioritize resources. In this paper, we propose the first metrics to assess heap overflow crashes based on both the attack aspect and the feasibility aspect. We further present HCSIFTER, a novel solution to automatically assess the exploitability of heap overflow instances under our metrics. Given a heap-based crash, HCSIFTER accurately detects heap overflows through dynamic execution without any source code or debugging information. Then it uses several novel methods to extract program execution information needed to quantify the severity of the heap overflow using our metrics. We have implemented a prototype HCSIFTER and applied it to assess nine programs with heap overflow vulnerabilities. HCSIFTER successfully reports that five heap overflow vulnerabilities are highly exploitable and two overflow vulnerabilities are unlikely exploitable. It also gave quantitatively assessments for other two programs. On average, it only takes about two minutes to assess one heap overflow crash. The evaluation result demonstrates both effectiveness and efficiency of HC Sifter.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450032","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115724
Kazi Zakia Sultana
Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security using traceable patterns and software metrics during development. The concept of traceable patterns is similar to design patterns but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. This study explores the performance of some code patterns in vulnerability prediction and compares them with traditional software metrics. We use the findings to build an effective vulnerability prediction model. We evaluate security vulnerabilities reported for Apache Tomcat, Apache CXF and three stand-alone Java web applications. We use machine learning and statistical techniques for predicting vulnerabilities using traceable patterns and metrics as features. We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics.
{"title":"Towards a software vulnerability prediction model using traceable code patterns and software metrics","authors":"Kazi Zakia Sultana","doi":"10.1109/ASE.2017.8115724","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115724","url":null,"abstract":"Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security using traceable patterns and software metrics during development. The concept of traceable patterns is similar to design patterns but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. This study explores the performance of some code patterns in vulnerability prediction and compares them with traditional software metrics. We use the findings to build an effective vulnerability prediction model. We evaluate security vulnerabilities reported for Apache Tomcat, Apache CXF and three stand-alone Java web applications. We use machine learning and statistical techniques for predicting vulnerabilities using traceable patterns and metrics as features. We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449191","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115660
Emilio Incerto, M. Tribastone, Catia Trubiani
A key challenge in software systems that are exposed to runtime variabilities, such as workload fluctuations and service degradation, is to continuously meet performance requirements. In this paper we present an approach that allows performance self-adaptation using a system model based on queuing networks (QNs), a well-assessed formalism for software performance engineering. Software engineers can select the adaptation knobs of a QN (routing probabilities, service rates, and concurrency level) and we automatically derive a Model Predictive Control (MPC) formulation suitable to continuously configure the selected knobs and track the desired performance requirements. Previous MPC approaches have two main limitations: i) high computational cost of the optimization, due to nonlinearity of the models; ii) focus on long-run performance metrics only, due to the lack of tractable representations of the QN's time-course evolution. As a consequence, these limitations allow adaptations with coarse time granularities, neglecting the system's transient behavior. Our MPC adaptation strategy is efficient since it is based on mixed integer programming, which uses a compact representation of a QN with ordinary differential equations. An extensive evaluation on an implementation of a load balancer demonstrates the effectiveness of the adaptation and compares it with traditional methods based on probabilistic model checking.
{"title":"Software performance self-adaptation through efficient model predictive control","authors":"Emilio Incerto, M. Tribastone, Catia Trubiani","doi":"10.1109/ASE.2017.8115660","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115660","url":null,"abstract":"A key challenge in software systems that are exposed to runtime variabilities, such as workload fluctuations and service degradation, is to continuously meet performance requirements. In this paper we present an approach that allows performance self-adaptation using a system model based on queuing networks (QNs), a well-assessed formalism for software performance engineering. Software engineers can select the adaptation knobs of a QN (routing probabilities, service rates, and concurrency level) and we automatically derive a Model Predictive Control (MPC) formulation suitable to continuously configure the selected knobs and track the desired performance requirements. Previous MPC approaches have two main limitations: i) high computational cost of the optimization, due to nonlinearity of the models; ii) focus on long-run performance metrics only, due to the lack of tractable representations of the QN's time-course evolution. As a consequence, these limitations allow adaptations with coarse time granularities, neglecting the system's transient behavior. Our MPC adaptation strategy is efficient since it is based on mixed integer programming, which uses a compact representation of a QN with ordinary differential equations. An extensive evaluation on an implementation of a load balancer demonstrates the effectiveness of the adaptation and compares it with traditional methods based on probabilistic model checking.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114474903","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115652
Yun Lin, Guozhu Meng, Yinxing Xue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, J. Dong
In this paper, we propose an approach to detecting project-specific recurring designs in code base and abstracting them into design templates as reuse opportunities. The mined templates allow programmers to make further customization for generating new code. The generated code involves the code skeleton of recurring design as well as the semi-implemented code bodies annotated with comments to remind programmers of necessary modification. We implemented our approach as an Eclipse plugin called MICoDe. We evaluated our approach with a reuse simulation experiment and a user study involving 16 participants. The results of our simulation experiment on 10 open source Java projects show that, to create a new similar feature with a design template, (1) on average 69% of the elements in the template can be reused and (2) on average 60% code of the new feature can be adopted from the template. Our user study further shows that, compared to the participants adopting the copy-paste-modify strategy, the ones using MICoDe are more effective to understand a big design picture and more efficient to accomplish the code reuse task.
{"title":"Mining implicit design templates for actionable code reuse","authors":"Yun Lin, Guozhu Meng, Yinxing Xue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, J. Dong","doi":"10.1109/ASE.2017.8115652","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115652","url":null,"abstract":"In this paper, we propose an approach to detecting project-specific recurring designs in code base and abstracting them into design templates as reuse opportunities. The mined templates allow programmers to make further customization for generating new code. The generated code involves the code skeleton of recurring design as well as the semi-implemented code bodies annotated with comments to remind programmers of necessary modification. We implemented our approach as an Eclipse plugin called MICoDe. We evaluated our approach with a reuse simulation experiment and a user study involving 16 participants. The results of our simulation experiment on 10 open source Java projects show that, to create a new similar feature with a design template, (1) on average 69% of the elements in the template can be reused and (2) on average 60% code of the new feature can be adopted from the template. Our user study further shows that, compared to the participants adopting the copy-paste-modify strategy, the ones using MICoDe are more effective to understand a big design picture and more efficient to accomplish the code reuse task.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124294232","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115676
Qi Xin, S. Reiss
We present our automated program repair technique ssFix which leverages existing code (from a code database) that is syntax-related to the context of a bug to produce patches for its repair. Given a faulty program and a fault-exposing test suite, ssFix does fault localization to identify suspicious statements that are likely to be faulty. For each such statement, ssFix identifies a code chunk (or target chunk) including the statement and its local context. ssFix works on the target chunk to produce patches. To do so, it first performs syntactic code search to find candidate code chunks that are syntax-related, i.e., structurally similar and conceptually related, to the target chunk from a code database (or codebase) consisting of the local faulty program and an external code repository. ssFix assumes the correct fix to be contained in the candidate chunks, and it leverages each candidate chunk to produce patches for the target chunk. To do so, ssFix translates the candidate chunk by unifying the names used in the candidate chunk with those in the target chunk; matches the chunk components (expressions and statements) between the translated candidate chunk and the target chunk; and produces patches for the target chunk based on the syntactic differences that exist between the matched components and in the unmatched components. ssFix finally validates the patched programs generated against the test suite and reports the first one that passes the test suite. We evaluated ssFix on 357 bugs in the Defects4J bug dataset. Our results show that ssFix successfully repaired 20 bugs with valid patches generated and that it outperformed five other repair techniques for Java.
{"title":"Leveraging syntax-related code for automated program repair","authors":"Qi Xin, S. Reiss","doi":"10.1109/ASE.2017.8115676","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115676","url":null,"abstract":"We present our automated program repair technique ssFix which leverages existing code (from a code database) that is syntax-related to the context of a bug to produce patches for its repair. Given a faulty program and a fault-exposing test suite, ssFix does fault localization to identify suspicious statements that are likely to be faulty. For each such statement, ssFix identifies a code chunk (or target chunk) including the statement and its local context. ssFix works on the target chunk to produce patches. To do so, it first performs syntactic code search to find candidate code chunks that are syntax-related, i.e., structurally similar and conceptually related, to the target chunk from a code database (or codebase) consisting of the local faulty program and an external code repository. ssFix assumes the correct fix to be contained in the candidate chunks, and it leverages each candidate chunk to produce patches for the target chunk. To do so, ssFix translates the candidate chunk by unifying the names used in the candidate chunk with those in the target chunk; matches the chunk components (expressions and statements) between the translated candidate chunk and the target chunk; and produces patches for the target chunk based on the syntactic differences that exist between the matched components and in the unmatched components. ssFix finally validates the patched programs generated against the test suite and reports the first one that passes the test suite. We evaluated ssFix on 357 bugs in the Defects4J bug dataset. Our results show that ssFix successfully repaired 20 bugs with valid patches generated and that it outperformed five other repair techniques for Java.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125346755","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115648
Soomin Kim, Markus Faerevaag, Minkyu Jung, S. Jung, DongYeop Oh, Jonghyup Lee, S. Cha
Binary lifting, which is to translate a binary executable to a high-level intermediate representation, is a primary step in binary analysis. Despite its importance, there are only few existing approaches to testing the correctness of binary lifters. Furthermore, the existing approaches suffer from low test coverage, because they largely depend on random test case generation. In this paper, we present the design and implementation of the first systematic approach to testing binary lifters. We have evaluated the proposed system on 3 state-of-the-art binary lifters, and found 24 previously unknown semantic bugs. Our result demonstrates that writing a precise binary lifter is extremely difficult even for those heavily tested projects.
{"title":"Testing intermediate representations for binary analysis","authors":"Soomin Kim, Markus Faerevaag, Minkyu Jung, S. Jung, DongYeop Oh, Jonghyup Lee, S. Cha","doi":"10.1109/ASE.2017.8115648","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115648","url":null,"abstract":"Binary lifting, which is to translate a binary executable to a high-level intermediate representation, is a primary step in binary analysis. Despite its importance, there are only few existing approaches to testing the correctness of binary lifters. Furthermore, the existing approaches suffer from low test coverage, because they largely depend on random test case generation. In this paper, we present the design and implementation of the first systematic approach to testing binary lifters. We have evaluated the proposed system on 3 state-of-the-art binary lifters, and found 24 previously unknown semantic bugs. Our result demonstrates that writing a precise binary lifter is extremely difficult even for those heavily tested projects.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117297993","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115706
Yuexing Wang, Min Zhou, Yu Jiang, Xiaoyu Song, M. Gu, Jiaguang Sun
To reduce the false positives of static analysis, many tools collect path constraints and integrate SMT solvers to filter unreachable execution paths. However, the accumulated calling and computing of SMT solvers are time and resource consuming. This paper presents TsmartLW, an alternate static analysis tool in which we implement a path constraint solving engine to speed up reachability determination. Within the engine, typical types of constraint-patterns are firstly defined based on an empirical study of a large number of code repositories. For each pattern, a constraint solving algorithm is designed and implemented. For each program, the engine predicts the most suitable strategy and then applies the strategy to solve path constraints. The experimental results on some well-known benchmarks and real-world applications show that TsmartLW is faster than some state-of-the-art static analysis tools. For example, it is 1.32× faster than CPAchecker and our engine is 369× faster than SMT solvers in solving path constraints. The demo video is available at https://www.youtube.com/watch?v=5c3ARhFclHA&t=2s.
{"title":"A static analysis tool with optimizations for reachability determination","authors":"Yuexing Wang, Min Zhou, Yu Jiang, Xiaoyu Song, M. Gu, Jiaguang Sun","doi":"10.1109/ASE.2017.8115706","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115706","url":null,"abstract":"To reduce the false positives of static analysis, many tools collect path constraints and integrate SMT solvers to filter unreachable execution paths. However, the accumulated calling and computing of SMT solvers are time and resource consuming. This paper presents TsmartLW, an alternate static analysis tool in which we implement a path constraint solving engine to speed up reachability determination. Within the engine, typical types of constraint-patterns are firstly defined based on an empirical study of a large number of code repositories. For each pattern, a constraint solving algorithm is designed and implemented. For each program, the engine predicts the most suitable strategy and then applies the strategy to solve path constraints. The experimental results on some well-known benchmarks and real-world applications show that TsmartLW is faster than some state-of-the-art static analysis tools. For example, it is 1.32× faster than CPAchecker and our engine is 369× faster than SMT solvers in solving path constraints. The demo video is available at https://www.youtube.com/watch?v=5c3ARhFclHA&t=2s.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126885627","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 : 2017-10-30DOI: 10.1109/ASE.2017.8115642
Jianjun Huang, Yousra Aafer, D. Perry, X. Zhang, Chen Tian
While smartphone« and mobile apps have been an integral part of our life, modern mobile apps tend to contain a lot of rarely used functionalities. For example, applications contain advertisements and offer extra features such as recommended news stories in weather apps. While these functionalities are not essential to an app, they nonetheless consume power, CPU cycles and bandwidth. In this paper, we design a UI driven approach that allows customizing an Android app by removing its unwanted functionalities. In particular, our technique displays the UI and allows the user to select elements denoting functionalities that she wants to remove. Using this information, our technique automatically removes all the code elements related to the selected functionalities, including all the relevant background tasks. The underlying analysis is a type system, in which each code element is tagged with a type indicating if it should be removed. From the UI hints, our technique infers types for all other code elements and reduces the app accordingly. We implement a prototype and evaluate it on 10 real-world Android apps. The results show that our approach can accurately discover the removable code elements and lead to substantial resource savings in the reduced apps.
{"title":"UI driven Android application reduction","authors":"Jianjun Huang, Yousra Aafer, D. Perry, X. Zhang, Chen Tian","doi":"10.1109/ASE.2017.8115642","DOIUrl":"https://doi.org/10.1109/ASE.2017.8115642","url":null,"abstract":"While smartphone« and mobile apps have been an integral part of our life, modern mobile apps tend to contain a lot of rarely used functionalities. For example, applications contain advertisements and offer extra features such as recommended news stories in weather apps. While these functionalities are not essential to an app, they nonetheless consume power, CPU cycles and bandwidth. In this paper, we design a UI driven approach that allows customizing an Android app by removing its unwanted functionalities. In particular, our technique displays the UI and allows the user to select elements denoting functionalities that she wants to remove. Using this information, our technique automatically removes all the code elements related to the selected functionalities, including all the relevant background tasks. The underlying analysis is a type system, in which each code element is tagged with a type indicating if it should be removed. From the UI hints, our technique infers types for all other code elements and reduces the app accordingly. We implement a prototype and evaluate it on 10 real-world Android apps. The results show that our approach can accurately discover the removable code elements and lead to substantial resource savings in the reduced apps.","PeriodicalId":382876,"journal":{"name":"2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124102894","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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