International audienceWe propose NOPOL, an approach to automatic repair of buggy conditional statements (i.e., if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of NOPOL consists of three major phases. First, NOPOL employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, NOPOL encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem; then a feasible solution to the SMT instance is translated back into a code patch. We evaluate NOPOL on 22 real-world bugs (16 bugs with buggy IF conditions and 6 bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy IF conditions and missing preconditions. We illustrate the capabilities and limitations of NOPOL using case studies of real bug fixes

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Abstract. Many problems in multi-agent systems can be represented as a Distributed Constraint Optimization Problem (DCOP) where the goal is to find the best assignment to variables in order to minimize the cost. More complex problems including several criteria can be represented as a Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) where the goal is to optimize several criteria at the same time. However, many problems are subject to changes over time and need to be represented as dynamic problems. In this paper, we formalize the Dynamic Multi-Objective Distributed Constraint Optimization Problem (DMO-DCOP) and introduce the first algorithm called DMOBB to handle changes in the number of objectives.

Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.