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With the emergence of mixed source software, the existing quality models are not able to better assess the community quality and autonomy controllability of mixed source software. To fill this gap, we propose a hierarchical model in this paper for quality assessment of mixed source software. In our model, the new attributes are proposed to meet the quality requirements of mixed source software based on the ISO/IEC 25010 standards, and a set of metrics are designed for the new attributes. The model evaluates the quality of mixed source software through quality attributes that have been quantified by the metrics. Applying our quality model to some mixed source software and comparing the model results with the actual situation, we verify whether our proposed two quality attributes, community intensity and autonomy controllability, can effectively assess the quality of mixed source software. The results of the experiments show that our model is indeed effective in assessing the quality of mixed source software. An important feature of our model is that the model has good flexibility, and the set of quality attributes and metrics can be adjusted freely, which provides a flexible and feasible way for various software quality assessment requirements.

The quality attributes of software architecture (SA) determine whether SA can be easily understood, tested, modified and so on, so quality-driven architecture evolution is important for keeping the viability and competitiveness. SA evolution is a process, and it contains multiple steps, such as SA quality measurement, SA modification, code co-evolution and so on. In order to guarantee that the software can be continuously improved and iteratively evolved in the future, we need to focus on all steps. However, most existing approaches only focus on one aspect, so they did not pay attention to how to finish the evolution process. In this paper, we propose a quality-driven iterative evolution approach for SA. This approach focuses on the whole process. In the first step, we use a quantitative approach to measure the architecture quality. Then, we construct the conflict graph to detect conflicts between evolution requirements to generate the final evolution scheme. In the third step, we modify architecture based on the evolution scheme. Finally, we co-evolve file dependency graph (FDG) based on the modified architecture. By focusing on the above steps, our approach can support a complete quality-driven architecture evolution process and obtain the maximum benefit in terms of the combined SA quality. We conduct our experiments with four open source projects, the experimental results indicate that our approach can improve SA quality, and our approach can effectively co-evolve the FDG to lay the foundation for the next evolution.

Jupyter Notebook is a popular tool among data analysts and scientists for working with data. It provides a way to combine code, documentation, and visualizations in a single, interactive environment, facilitating code reuse. While code reuse can improve programming efficiency, it can also decrease readability, security, and overall performance. We conduct a large-scale exploratory study of code reuse practices in the Jupyter Notebook development community on the Stack Overflow platform to understand the potential negative impacts of code reuse. Our findings identified 1,097,470 Jupyter Notebook clone pairs that reuse Stack Overflow code snippets, and the average code snippet has 7.91 code quality violations. Through our research, we gain insight into the reasons behind Jupyter Notebook developers’ decision to reuse code and the potential drawbacks of this practice.

As smart contracts process digital assets, their security is essential for blockchain applications. Many approaches have been proposed to detect smart contract vulnerabilities. Studies show that few of the reported vulnerabilities are exploited and hypothesize that many of the reported vulnerabilities are false positives. However, no follow-up study is performed to confirm the hypothesis and understand why the reported vulnerabilities are not exploited. In this study, we first collect 136,969 unique real-world smart contracts and analyze them using four vulnerability detectors, namely Oyente, SmartCheck, Slither, and SolDetector. Then, we apply Strauss’ grounded theory approach to manually analyze the source code of the smart contracts reported as vulnerable to recognizing false positives and understand the reasons for false results. In addition, we analyze the transaction logs of the smart contracts reported as vulnerable to identifying and understanding their exploitations. Our results show that 75.37% of the 4,364 smart contracts reported as vulnerable are false positives, and eleven reasons are causing the false positives. After analyzing the 4,106,134 transaction logs of the contracts reported as vulnerable, we find that vulnerabilities of only 67 (0.015%) of the contracts have been exploited in history. We also identify six reasons for demotivating and preventing the attackers from exploiting the vulnerabilities. Our results reveal that state-of-the-art smart contract vulnerability detectors primarily treat the smart contracts as yet another application developed using Object Oriented (OO) languages when analyzing and reporting the smart contract vulnerabilities. Without considering the specific design principles of the Solidity programming language and the characteristics of smart contracts' application scenarios and execution environments, many of the reported vulnerabilities are not exploitable or not cost-effective to be exploited by adversaries.

Organic-inorganic halide perovskite solar cells (PSCs) have attracted much attention in recent years due to their simple manufacturing process, low cost, and high efficiency. So far, all efficient organic-inorganic halide PSCs are mainly made of polycrystalline perovskite films. There are transmission barriers and high-density defects on the surface, interface, and grain boundary of the films. Among them, the deep-level traps caused by specific charged defects are the main non-radiative recombination centers, which is the most important factor in limiting the photoelectric conversion efficiency of PSCs devices to the Shockley-Queisser (S-Q) theoretical efficiency limit. Therefore, it is imperative to select appropriate passivation materials and passivation strategies to effectively eliminate defects in perovskite films to improve their photovoltaic performance and stability. There are various passivation strategies for different components of PSCs, including interface engineering, additive engineering, antisolvent engineering, dopant engineering, etc. In this review, we summarize a large number of defect passivation work to illustrate the latest progress of different types of passivators in regulating the morphology, grain boundary, grain size, charge recombination, and defect density of states of perovskite films. In addition, we discuss the inherent defects of key materials in carrier transporting layers and the corresponding passivation strategies to further optimize PSCs components. Finally, some perspectives on the opportunities and challenges of PSCs in future development are highlighted.