Modern web applications are built on top of web frameworks, which offer benefits such as feature reuse and productivity improvement. By relying on web frameworks, developers can safely handle the multiple needs of real-world web applications. Despite the benefits of web frameworks, they are not intended to address all requirements to create web applications. In particular, the Django web framework provides a rich software ecosystem of reusable applications to support developers with advanced development aspects, such as modern authentication, caching, and deployment. However, currently, it is not clear what solutions developers can reuse or how active and reliable this ecosystem really is. To overcome this gap, this paper proposes an empirical study to assess the Django ecosystem of reusable applications. We analyze 487 reusable applications to understand their target domain, activity level, frequency of tests, and compatibility. We find that reusable applications are mostly concentrated in three categories: developer tools (27.8%), user interface (17.1%), and persistence (15.9%). Overall, these categories have distinct characteristics; for example, developer tools have the highest concentration of young and inactive applications, while persistence applications are the oldest. The reusable applications are 7.7 years old but have 10.17 months without commits, on the median. We also find that 80% of the reusable applications include the supported Python versions, 64% include the Django versions, and only 61% include both Python and Django versions. We detect that upgrading to recent versions of Django and Python may not be supported in some reusable applications, but compatibility issues become less problematic in recent Django and Python versions. Finally, based on our findings, we provide implications for practitioners and researchers.

Deep generative models have been widely used in molecular generation tasks because they can save time and cost in drug development compared with traditional methods. Previous studies based on generative adversarial network (GAN) models typically employ reinforcement learning (RL) to constrain chemical properties, resulting in efficient and novel molecules. However, such models have poor performance in generating molecules due to instability in training. Therefore, quantitative evaluation of existing molecular generation models, especially GAN models, is necessary. This study aims to evaluate the performance of discrete GAN models using RL in molecular generation tasks and explore the impact of different factors on model performance. Through evaluation experiments on QM9 and ZINC datasets, the results show that noise sampling distributions, training epochs, and training data volumes can affect the performance of molecular generation. Finally, we provide strategies for stable training and improved performance for GAN models.

In this article the affiliation details for Simon Butler, Jonas Gamalielsson, and Björn Lundell were incorrectly given as '

Boundary value analysis (BVA) is a common technique in software testing that uses input values that lie at the boundaries where significant changes in behavior are expected. This approach is widely recognized and used as a natural and effective strategy for testing software. Test coverage is one of the criteria to measure how much the software execution paths are covered by the set of test cases. This paper focuses on evaluating test coverage with respect to BVA by defining a metric called boundary coverage distance (BCD). The BCD metric measures the extent to which a test set covers the boundaries. In addition, based on BCD, we consider the optimal test input generation to minimize BCD under the random testing scheme. We propose three algorithms, each representing a different test input generation strategy, and evaluate their fault detection capabilities through experimental validation. The results indicate that the BCD-based approach has the potential to generate boundary values and improve the effectiveness of software testing.