In many domains, there are many examples and far fewer labels for those examples; e.g. we may have access to millions of lines of source code, but access to only a handful of warnings about that code. In those domains, semi-supervised learners (SSL) can extrapolate labels from a small number of examples to the rest of the data. Standard SSL algorithms use ``weak'' knowledge (i.e. those not based on specific SE knowledge) such as (e.g.) co-train two learners and use good labels from one to train the other. Another approach of SSL in software analytics is potentially use ``strong'' knowledge that use SE knowledge. For example, an often-used heuristic in SE is that unusually large artifacts contain undesired properties (e.g. more bugs). This paper argues that such ``strong'' algorithms perform better than those standard, weaker, SSL algorithms. We show this by learning models from labels generated using weak SSL or our ``stronger'' FRUGAL algorithm. In four domains (distinguishing security-related bug reports; mitigating bias in decision-making; predicting issue close time; and (reducing false alarms in static code warnings), FRUGAL required only 2.5% of the data to be labeled yet out-performed standard semi-supervised learners that relied on (e.g.) some domain-independent graph theory concepts. Hence, for future work, we strongly recommend the use of strong heuristics for semi-supervised learning for SE applications. To better support other researchers, our scripts and data are on-line at https://github.com/HuyTu7/FRUGAL.
An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications
超启发式算法是一种新的自适应混合元启发式算法，以获得一个通用的算法来解决优化问题。这项工作的重点是选择类型的超启发式，称为指数蒙特卡罗计数器（EMCQ）。当前实现依赖于无记忆选择，这可能是适得其反的，因为所选择的搜索运算符可能（历史上）不是当前搜索实例的最佳执行运算符。为了解决这个问题，我们建议将内存集成到EMCQ中，使用基于Q学习机制的强化学习（称为Q-EMCQ）进行组合t-wise测试集生成。组合测试生成在工业程序中的有限应用可能会影响Q-EMCQ等技术的使用。因此，有必要针对相关的工业软件评估这种方法，目的是显示覆盖代码以及查找错误所需的交互程度。我们将Q-EMCQ应用于37个用功能框图（FBD）语言编写的实际工业程序，这些程序用于在Bombardier Transportation Sweden AB开发训练控制管理系统。研究结果表明，Q-EMCQ是一种有效的测试用例生成技术。此外，与处理最小化问题的t-明智测试集生成不同，我们还将Q-EMCQ进行了涉及一般模块聚类的最大化问题，以证明我们方法的有效性。
Supplementary Material for the Information Sciences Paper: An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t-way Test Suite Generation
LLM-Based Invariant Testing for Software Functional Bugs
Northeastern University（东北大学）；University of California, Davis（加州大学戴维斯分校）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
