Context: Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects.Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. Objective: The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classication. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classiers. Method: We collect more than 50,000 bug reports from ve development projects from two companies in dierent domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. Results: We show that SG scales to large scale industrial application and that it outperforms the use of individual classiers for bug assignment, reaching prediction accuracies from 50% to 90% when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. Conclusions: We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.

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Fast-growing broad-leaf tree species can serve as feedstocks for production of bio-based chemicals and fuels through biochemical conversion of wood to monosaccharides. This conversion is hampered by the xylan acetylation pattern. To reduce xylan acetylation in the wood, the Hypocrea jecorina acetyl xylan esterase (HjAXE) from carbohydrate esterase (CE) family 5 was expressed in hybrid aspen under the control of the wood-specific PtGT43B promoter and targeted to the secretory pathway. The enzyme was predicted to deacetylate polymeric xylan in the vicinity of cellulose due to the presence of a cellulose-binding module. Cell-wall-bound protein fractions from developing wood of transgenic plants were capable of releasing acetyl from finely ground wood powder, indicative of active AXE present in cell walls of these plants, whereas no such activity was detected in wild-type plants. The transgenic lines grew in height and diameter as well as wild-type trees, whereas their internodes were slightly shorter, indicating higher leaf production. The average acetyl content in the wood of these lines was reduced by 13%, mainly due to reductions in di-acetylated xylose units, and in C-2 and C-3 mono-acetylated xylose units. Analysis of soluble cell wall polysaccharides revealed a 4% reduction in the fraction of xylose units and an 18% increase in the fraction of glucose units, whereas the contents of cellulose and lignin were not affected. Enzymatic saccharification of wood from transgenic plants resulted in 27% higher glucose yield than for wild-type plants. Brunauer-Emmett-Teller (BET) analysis and Simons' staining pointed toward larger surface area and improved cellulose accessibility for wood from transgenic plants compared to wood from wild-type plants, which could be achieved by HjAXE deacetylating xylan bound to cellulose. The results show that CE5 family can serve as a source of enzymes for in planta reduction of recalcitrance to saccharification.

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