A build system converts source code, libraries and other data into executable programs by orchestrating the execution of compilers and other tools. The whole building process is managed by a software build system, such as Make, Ant, CMake, Maven, Scons, and QMake. Many studies have investigated bugs and fixes in several systems, but to our best knowledge, none focused on bugs in build systems. One significant feature of software build systems is that they should work on various platforms, i.e., various operating systems (e.g., Windows, Linux), various development environments (e.g., Eclipse, Visual Studio), and various programming languages (e.g., C, C++, Java, C#), so the study of software build systems deserves special consideration. In this paper, we perform an empirical study on bugs in software build systems. We analyze four software build systems, Ant, Maven, CMake and QMake, which are four typical and widely-used software build systems, and can be used to build Java, C, C++ systems. We investigate their bug database and code repositories, randomly sample a set of bug reports and their fixes (800 bugs reports totally, and 199, 250, 200, and 151 bug reports for Ant, Maven, CMake and QMake, respectively), and manually assign them into various categories. We find that 21.35% of the bugs belong to the external interface category, 18.23% of the bugs belong to the logic category, and 12.86% of the bugs belong to the configuration category. We also investigate the relationship between bug categories and bug severities, bug fixing time, and number of bug comments.

In this paper we design a fast fabric defect detection framework (Fast-DDF) based on gray histogram back-projection, which adopts end to end multi-convoluted network model to realize defect classification. First, the back-projection image is established through the gray histogram on fabric image, and the closing operation and adaptive threshold segmentation method are performed to screen the impurity information and extract the defect regions. Then, the defect images segmented by the Fast-DDF are marked and normalized into the multi-layer convolutional neural network for training. Finally, in order to solve the problem of difficult adjustment of network model parameters and long training time, some strategies such as batch normalization of samples and network fine tuning are proposed. The experimental results on the TILDA database show that our method can deal with various defect types of textile fabrics. The average detection accuracy with a higher rate of 96.12% in the database of five different defects, and the single image detection speed only needs 0.72s.