No abstract

Probabilistic software analysis aims at quantifying how likely a target event is to occur during program execution. Current approaches rely on symbolic execution to identify the conditions to reach the target event and try to quantify the fraction of the input domain satisfying these conditions. Precise quantification is usually limited to linear constraints, while only approximate solutions can be provided in general through statistical approaches. However, statistical approaches may fail to converge to an acceptable accuracy within a reasonable time.We present a compositional statistical approach for the efficient quantification of solution spaces for arbitrarily complex constraints over bounded floating-point domains. The approach leverages interval constraint propagation to improve the accuracy of the estimation by focusing the sampling on the regions of the input domain containing the sought solutions. Preliminary experiments show significant improvement on previous approaches both in results accuracy and analysis time.

We present a technique for generating efficient monitors for ω-regular-languages. We show how Büchi automata can be reduced in size and transformed into special, statistically optimal nondeterministic finite state machines, called binary transition tree finite state machines (BTT-FSMs), which recognize precisely the minimal bad prefixes of the original ω-regular-language. The presented technique is implemented as part of a larger monitoring framework and is available for download.

Spectrum-based Bayesian reasoning can effectively rank candidate fault locations based on passing/failing test cases, but the diagnostic quality highly depends on the size and diversity of the underlying test suite. As test suites in practice often do not exhibit the necessary properties, we present a technique to extend existing test suites with new test cases that optimize the diagnostic quality. We apply probability theory concepts to guide test case generation using entropy, such that the amount of uncertainty in the diagnostic ranking is minimized. Our ENTBUG prototype extends the search-based test generation tool EVOSUITE to use entropy in the fitness function of its underlying genetic algorithm, and we applied it to seven real faults. Empirical results show that our approach reduces the entropy of the diagnostic ranking by 49% on average (compared to using the original test suite), leading to a 91% average reduction of diagnosis candidates needed to inspect to find the true faulty one.

Many programs can be configured through dynamic and/or static selection of configuration variables. A software product line (SPL), for example, specifies a family of programs where each program is defined by a unique combination of features. Systematically testing SPL programs is expensive as it can require running each test against a combinatorial number of configurations. Fortunately, a test is often independent of many configuration variables and need not be run against every combination. Configurations that are not required for a test can be pruned from execution. This paper presents SPLat, a new way to dynamically prune irrelevant configurations: the configurations to run for a test can be determined during test execution by monitoring accesses to configuration variables. SPLat achieves an optimal reduction in the number of configurations and is lightweight compared to prior work that used static analysis and heavyweight dynamic execution. Experimental results on 10 SPLs written in Java show that SPLat substantially reduces the total test execution time in many cases. Moreover, we demonstrate the scalability of SPLat by applying it to a large industrial code base written in Ruby on Rails.