Using a specific machine learning technique, this paper proposes a way to identify suspicious statements during debugging. The technique is based on principles similar to Tarantula but addresses its main flaw: its difficulty to deal with the presence of multiple faults as it assumes that failing test cases execute the same fault(s). The improvement we present in this paper results from the use of C4.5 decision trees to identify various failure conditions based on information regarding the test cases' inputs and outputs. Failing test cases executing under similar conditions are then assumed to fail due to the same fault(s). Statements are then considered suspicious if they are covered by a large proportion of failing test cases that execute under similar conditions. We report on a case study that demonstrates improvement over the original Tarantula technique in terms of statement ranking. Another contribution of this paper is to show that failure conditions as modeled by a C4.5 decision tree accurately predict failures and can therefore be used as well to help debugging.18th IEEE International Symposium on Software Reliability Engineering

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We demonstrate the successful application of the state-of-the-art AstraZeneca in-house and XtalPi cloud-based virtual polymorph screening workflows in support of stable form selection of crystalline oxabispidine AZD1305, a pharmaceutical compound. Experimental solid form screening had found two polymorphic forms, A and B, with physical stabilities that appeared to be extremely close at ambient temperature. Such observation may make experimental and in silico support of the solid form selection a challenging task. Both computational approaches correctly predicted the ranking and geometry of the stable form B at 0 K. This level of information would be important and sufficient for project support at the late discovery stage. However, metastable form A was predicted by both workflows to be considerably less stable than form B, separated by multiple virtual forms in the lattice energy landscapes. In order to account for the experimentally observed close physical stabilities of forms A and B at ambient temperature, calculation of the free-energy landscape was performed using pseudo-supercritical path method. This allowed the demonstration that, while form B is significantly more stable at 0 K, the two forms display a very close physical stability at ambient temperature. The current work highlights the importance of using advanced virtual polymorph screening to get a more comprehensive insight into identifying the most stable form of a pharmaceutical compound under different experimental conditions.

The Hamiltonian dynamics is adopted to solve the eigenvalue problem for transverse vibrations of axially moving strings. With the explicit Hamiltonian function the canonical equation of the free vibration is derived. Non-singular modal functions are obtained through a linear, symplectic eigenvalue analysis, and the symplectic-type orthogonality conditions of modes are derived. Stability of the transverse motion is examined by means of analyzing the eigenvalues and their bifurcation, especially for strings transporting with the critical speed. It is pointed out that the motion of the string does not possess divergence instability at the critical speed due to the weak interaction between eigenvalue pairs. The expansion theorem is applied with the non-singular modal functions to solve the displacement response to free and forced vibrations. It is demonstrated that the modal functions can be used as the base functions for solving linear and nonlinear vibration problems.

Cocrytals as a solid form technology for improving physicochemical properties have grown increasing popular in the pharmaceutical, nutraceutical, and agrochemical industries. However, the list of potential coformers contains hundreds of...