Batch, conversational, and incremental compilers

Katzan, Harry

doi:10.1145/1476793.1476813

Cited by 12 publications

(17 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With this speed increase, incremental analyses may be used continuously by blocking the developer whenever the analysis runs. Incremental code compilation [17] is one such popular incremental, continuous analysis integrated into many IDEs. However, many analyses cannot be made incremental efficiently because small code changes may force these analyses to explore large, distant parts of the code.…”

Section: Discussionmentioning

confidence: 99%

“…The results could be shown to the developer immediately to reduce wasted time [1,17,24], or less frequently to avoid distracting and annoying the developer [2]. Codebase Replication supports both.…”

Section: Introductionmentioning

confidence: 87%

“…Incremental code compilation [17] has been integrated in most modern IDEs, such as Eclipse 5 . Eclipse Java compiler warnings and errors 6 , including the Nullness type system, are closely integrated into the incremental compiler.…”

Section: Related Workmentioning

confidence: 99%

“…can lead to wasted effort or confusion [1,17,24]. Ideally, the developer learns the implications of a change as soon as the change is made.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Making offline analyses continuous

Muşlu

Brun

Ernst

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

View full text Add to dashboard Cite

Developers use analysis tools to help write, debug, and understand software systems under development. A developer's change to the system source code may affect analysis results. Typically, to learn those effects, the developer must explicitly initiate the analysis. This may interrupt the developer's workflow and/or the delay until the developer learns the implications of the change. The situation is even worse for impure analyses -ones that modify the code on which it runs -because such analyses block the developer from working on the code. This paper presents Codebase Replication, a novel approach to easily convert an offline analysis -even an impure one -into a continuous analysis that informs the developer of the implications of recent changes as quickly as possible after the change is made. Codebase Replication copies the developer's codebase, incrementally keeps this copy codebase in sync with the developer's codebase, makes that copy codebase available for offline analyses to run without disturbing the developer and without the developer's changes disturbing the analyses, and makes analysis results available to be presented to the developer.We have implemented Codebase Replication in Solstice, an opensource, publicly-available Eclipse plug-in. We have used Solstice to convert three offline analyses -FindBugs, PMD, and unit testing -into continuous ones. Each conversion required on average 436 NCSL and took, on average, 18 hours. Solstice-based analyses experience no more than 2.5 milliseconds of runtime overhead per developer action.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

“…can lead to wasted effort or confusion [1,17,24]. Ideally, the developer learns the implications of a change as soon as the change is made.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Making offline analyses continuous

Muşlu

Brun

Ernst

et al. 2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

View full text Add to dashboard Cite

show abstract

“…Previous work on incremental view maintenance [6,11] will guide our research. In other domains, incremental computation has been shown to greatly speed up data structures [33] and code compilation [17].…”

Section: Continuous Data Testingmentioning

confidence: 99%

Data debugging with continuous testing

Muşlu

Brun

Meliou

2013

Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering

View full text Add to dashboard Cite

Today, systems rely as heavily on data as on the software that manipulates those data. Errors in these systems are incredibly costly, annually resulting in multi-billion dollar losses, and, on multiple occasions, in death. While software debugging and testing have received heavy research attention, less effort has been devoted to data debugging: discovering system errors caused by well-formed but incorrect data. In this paper, we propose continuous data testing: using otherwise-idle CPU cycles to run test queries, in the background, as a user or database administrator modifies a database. This technique notifies the user or administrator about a data bug as quickly as possible after that bug is introduced, leading to at least three benefits: (1) The bug is discovered quickly and can be fixed before it is likely to cause a problem. (2) The bug is discovered while the relevant change is fresh in the user's or administrator's mind, increasing the chance that the underlying cause of the bug, as opposed to only the discovered side-effect, is fixed. (3) When poor documentation or company policies contribute to bugs, discovering the bug quickly is likely to identify these contributing factors, facilitating updating documentation and policies to prevent similar bugs in the future. We describe the problem space and potential benefits of continuous data testing, our vision for the technique, challenges we encountered, and our prototype implementation for PostgreSQL.

show abstract

References

2010

Trustworthy Compilers

View full text Add to dashboard Cite

Batch, conversational, and incremental compilers

Cited by 12 publications

References 7 publications

Making offline analyses continuous

Making offline analyses continuous

Data debugging with continuous testing

References

Contact Info

Product

Resources

About