Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets.

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings

Optimising Spectrum Based Fault Localisation for Single Fault Programs Using Specifications.

Spectrum based fault localisation determines how suspicious a line of code is with respect to being faulty as a function of a given test suite. Outstanding problems include identifying properties that the test suite should satisfy in order to improve fault localisation effectiveness subject to a given measure, and developing methods that generate these test suites efficiently. We address these problems as follows. First, when single bug optimal measures are being used with a single-fault program, we identify a formal property that the test suite should satisfy in order to optimise fault localisation. Second, we introduce a new method which generates test data that satisfies this property. Finally, we empirically demonstrate the utility of our implementation at fault localisation on SV-COMP benchmarks and the tcas program, demonstrating that test suites can be generated in almost a second with a fault identified after inspecting under 1% of the program.</p

Optimising spectrum based fault localisation for single fault programs using specifications

Spectrum based fault localisation determines how suspicious a line of code is with respect to being faulty as a function of a given test suite. Outstanding problems include identifying properties that the test suite should satisfy in order to improve fault localisation effectiveness subject to a given measure, and developing methods that generate these test suites efficiently. We address these problems as follows. First, when single bug optimal measures are being used with a single-fault program, we identify a formal property that the test suite should satisfy in order to optimise fault localisation. Second, we introduce a new method which generates test data that satisfies this property. Finally, we empirically demonstrate the utility of our implementation at fault localisation on SV-COMP benchmarks and the tcas program, demonstrating that test suites can be generated in almost a second with a fault identified after inspecting under 1% of the program.</p

Probabilistic Fault Localisation

Efficient fault localisation is becoming increasingly important as software grows in size and complexity. In this paper we present a new formal framework, denoted probabilistic fault localisation (pfl), and compare it to the established framework of spectrum based fault localisation (sbfl). We formally prove that pfl satisfies some desirable properties which sbfl does not, empirically demonstrate that pfl is signifuicantly more effective at finding faults than all known sbfl measures in large scale experimentation, and show pfl has comparable efficiency. Results show that the user investigates 37% more code (and finds a fault immediately in 27% fewer cases) when using the best performing sbfl measures, compared to the pfl framework. Furthermore, we show that it is theoretically impossible to design strictly rational sbfl measures that outperform pfl techniques on a large set of benchmarks