ConflictJS: finding and understanding conflicts between JavaScript libraries

ConflictJS: finding and understanding conflicts between JavaScript libraries Patra et al., ICSE'18 The JavaScript ecosystem is fertile ground for dependency hell. With so many libraries being made available and the potential for global namespace clashes, it’s easy for libraries to break each other. Sometimes in an obvious to spot way (that’s a good day!), and … Continue reading ConflictJS: finding and understanding conflicts between JavaScript libraries

Debugging with intelligence via probabilistic inference

Debugging with intelligence via probabilistic inference Xu et al., ICSE'18 Xu et al. have built a automated debugger that can take a single failing test execution, and with minimal interaction from a human, pinpoint the root cause of the failure. What I find really exciting about it, is that instead of brute force there’s a … Continue reading Debugging with intelligence via probabilistic inference

DeepTest: automated testing of deep-neural-network-driven autonomous cars

DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE'18 How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under … Continue reading DeepTest: automated testing of deep-neural-network-driven autonomous cars

Popular is cheaper: curtailing memory costs in interactive analytics engines

Popular is cheaper: curtailing memory costs in interactive analytics engines Ghosh et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). We’re sticking with the optimisation of data analytics today, but at the other end of … Continue reading Popular is cheaper: curtailing memory costs in interactive analytics engines

Analytics with smart arrays: adaptive and efficient language-independent data

Analytics with smart arrays: adaptive and efficient language-independent data Psaroudakis et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). We’re going lower-level today, with a look at some work on adaptive data structures by Oracle. … Continue reading Analytics with smart arrays: adaptive and efficient language-independent data

Medea: scheduling of long running applications in shared production clusters

Medea: scheduling of long running applications in shared production clusters Garefalakis et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). We’re sticking with schedulers today, and a really interesting system called Medea which is designed … Continue reading Medea: scheduling of long running applications in shared production clusters

Optimus: an efficient dynamic resource scheduler for deep learning clusters

Optimus: an efficient dynamic resource scheduler for deep learning clusters Peng et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). It’s another paper promising to reduce your deep learning training times today. But instead of … Continue reading Optimus: an efficient dynamic resource scheduler for deep learning clusters