Characterizing and detecting performance bugs for smartphone applications

Liu, Yepang; Xu, Chang; Cheung, Shing-Chi

doi:10.1145/2568225.2568229

Cited by 237 publications

(178 citation statements)

References 13 publications

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“…We found that 251 places in 51 projects execute long-running operations in UI event thread. This also confirms the findings of a recent study by Liu et al [27] that shows that 21% of reported responsiveness bugs in an Android corpus arise because developers tend to forget encapsulating long-running operations in AsyncTask.…”

Section: Introductionsupporting

confidence: 90%

“…Liu et al [27] empirically study three performance bug patterns in Android apps, one of which is running longrunning operations in main thread. They also propose an approach to detect such operations statically.…”

Section: Related Workmentioning

confidence: 99%

“…Previous research [27,37] shows that many Android apps suffer from poor responsiveness and one of the primary reaPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Retrofitting concurrency for Android applications through refactoring

Lin

Radoi

Dig

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Running compute-intensive or blocking I/O operations in the UI event thread of smartphone apps can severely degrade responsiveness. Despite the fact that Android supports writing concurrent code via AsyncTask, we know little about how developers use AsyncTask to improve responsiveness. To understand how AsyncTask is used/underused/misused in practice, we first conduct a formative study using a corpus of 104 open-source Android apps comprising 1.34M SLOC. Our study shows that even though half of the apps use AsyncTask, there are hundreds of places where they missed opportunities to encapsulate long-running operations in AsyncTask. Second, 46% of the usages are manually refactored. However, the refactored code contains concurrency bugs (such as data races) and performance bugs (concurrent code still executes sequentially).Inspired by these findings, we designed, developed, and evaluated Asynchronizer, an automated refactoring tool that enables developers to extract long-running operations into AsyncTask. Asynchronizer uses a points-to static analysis to determine the safety of the transformation. Our empirical evaluation shows that Asynchronizer is (i) highly applicable, (ii) accurate, (iii) safer than manual refactoring (iv) it saves development effort, (v) its results have been accepted by the open-source developers. This shows that Asynchronizer is useful.

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Section: Introductionsupporting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retrofitting concurrency for Android applications through refactoring

Lin

Radoi

Dig

2014

Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

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“…They all have different focuses. Some of them [49] compare the qualitative difference between performance bugs and non-performance bugs across impact, context, fix and fix validation; some of them [21] look at how performance bugs are introduced, how performance bugs manifest, and how performance bugs are fixed; some of them [32] focuses on performance bugs in smart-phone applications. Different from all previous studies, our study aims to provide guidance to performance problem diagnosis, and hence focuses on how performance problems are noticed and reported by end users.…”

Section: Empirical Study Of Performance Bugsmentioning

confidence: 99%

Statistical debugging for real-world performance problems

Song

2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause are sorely needed.The state of the art of performance diagnosis is preliminary. Profiling can identify the functions that consume the most computation resources, but can neither identify the ones that waste the most resources nor explain why. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question.In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the over- * Shan is now with University of Chicago.

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“…They all have different focuses. Some of them [41] compare the qualitative difference between performance bugs and non-performance bugs across impact, context, fix and fix validation; some of them [18] look at how performance bugs are introduced, how performance bugs manifest, and how performance bugs are fixed; some of them [25] focuses on performance bugs in smart-phone applications. Different from all previous studies, our study aims to provide guidance to performance problem diagnosis, and hence focuses on how performance problems are noticed and reported by end users.…”

Section: Empirical Study Of Performance Bugsmentioning

confidence: 99%

Statistical debugging for real-world performance problems

SongLinhai

2014

SIGPLAN Not.

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Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause is sorely needed.The state of the art of performance diagnosis is preliminary. Profiling can tell where computation resources are spent, but not where and why the resources are wasted. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question.In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the overhead of run-time performance diagnosis without extending the diagnosis latency.

show abstract

Characterizing and detecting performance bugs for smartphone applications

Cited by 237 publications

References 13 publications

Retrofitting concurrency for Android applications through refactoring

Retrofitting concurrency for Android applications through refactoring

Statistical debugging for real-world performance problems

Statistical debugging for real-world performance problems

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