Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code, an important capability for building accurate prediction models. In this paper, we develop a novel prediction model which is capable of automatically learning features for representing source code and using them for defect prediction. Our prediction system is built upon the powerful deep learning, tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. An evaluation on two datasets, one from open source projects contributed by Samsung and the other from the public PROMISE repository, demonstrates the effectiveness of our approach for both within-project and cross-project predictions. CCS CONCEPTS• Software and its engineering → Software creation and management; KEYWORDSSoftware engineering, software analytics, defect prediction ACM Reference Format:

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Abstract:The pretreatment processes consisting of grinding followed by roasting were investigated to improve the selective leaching of Nd and Dy from neodymium-iron-boron (NdFeB) magnet scraps. The peaks of Nd(OH)3 and Fe were observed in XRD results after grinding with NaOH as the amount of water addition increased to 5 cm 3 . These resultsindicate that the components of Nd and Fe in NdFeB magnet could be changed successfully into Nd(OH)3 and Fe, respectively. In the roasting tests using the ground product, with increasing roasting temperature to 500 °C, the peaks of Nd(OH)3 and Fe disappeared while those of Nd2O3 and Fe2O3 were shown. The peaks of NdFeO3 in the sample roasted at 600 °C were observed in the XRD pattern. Consequently, 94.2%, 93.1%, 1.0% of Nd, Dy, Fe were leached at 400 rpm and 90 °C in 1 kmol· m −3 acetic acid solution with 1% pulp density using a sample prepared under the following conditions: 15 in stoichiometric molar ratio of NaOH:Nd, 550 rpm in rotational grinding speed, 5 cm 3 in water addition, 30 min in grinding time, 400 °C and 2 h in roasting temperature and time. The results indicate that the selective leaching of Nd and Dy from NdFeB magnet could be achieved successfully by grinding and then roasting treatments. OPEN ACCESSMetals 2015, 5 1307

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