Selective sequestration of milkweed (Asclepias sp.) cardenolides inOncopeltus fasciatus (Dallas) (Hemiptera: Lygaeidae)

The cardenolide content of the gut, wings, and fat body ofOncopeltus fasciatus was examined. The female fat body contained 4–5% of the total cardenolide content of the insect. The cardenolide content of male fat body, and gut and wings of both sexes was below the detection limit of the cardenolide assay. Thin-layer chromatography was used to determine the cardenolide array of various tissues and secretions ofO. fasciatus reared on seeds of a single species of milkweed (A. Speciosa) and adult extracts and dorsolateral space fluid ofO. fasciatus reared on seeds of two species of milkweed with different cardenolide arrays (A. speciosa andA. syriaca). Our results indicate that cardenolides are not sequestered in the insect simply on the basis of polarity and that metabolism and differential excretion of cardenolides are involved in the sequestration of cardenolides inO. fasciatus. The similarities in the cardenolide profiles ofO. fasciatus reared on different food sources, and tissues ofO. fasciatus reared on a single food source indicates that there is regulation of the cardenolide array inO. Fasciatus.     

Sequestration of host plant-derived compounds by geometrid moth, Milionia basalis, toxic to a predatory stink bug, Eocanthecona furcellata

A predatory stink bug, Eocanthecona furcellata, died after feeding on Milionia basalis larvae. The compounds toxic to E. furcellata were isolated from the hemolymph of M. basalis larvae and identified as inumakilactone A, nagilactone C, and nagilactone C glucoside. The concentrations of inumakilactone A, nagilactone C, and nagilactone C glucoside in the hemolymph of the final instar larvae were 130, 50, and 770 g/ml, respectively. Nagilactone C showed the highest insecticidal activity against second-instar nymphs of E. furcellata, while nagilactone C glucoside showed the lowest, one twentieth of that of nagilactone C. When mixed compounds were given at the same concentrations as those in hemolymph of M. basalis, all nymphs of E. furcellata died with in three days. Inumakilactone A and nagilactone C were found to be in the leaves of podocarp, Podocarpus macrophyllus, the only host plant of M. basalis, at concentrations of 13 and 175 g/g fresh weight, respectively. However, no nagilactone C glucoside was detected in the leaves of this species. These results suggested that M. basalis may transform nagilactone C to its glucoside.     

Pheromones of milkweed bugs (Heteroptera: Lygaeidae) attract wayward plant bugs: Phytocoris mirid sex pheromone

The synthetic aggregation pheromone of the large milkweed bug, Oncopeltus fasciatus (Dallas) (Lygaeinae), also attracted males of the plant bug, Phytocoris difficilis Knight (Miridae). Field testing partial blends against the six-component blend comprising the Oncopeltus pheromone showed that cross-attraction of P. difficilis males was due to synergism between (E)-2-octenyl acetate and (E,E)-2,4-hexadienyl acetate. Hexyl acetate was abundant in the metathoracic scent gland (MSG) secretion of P. difficilis males, but because female P. difficilis could not initially be found in the field, further combinatorial tests were guided by prior research on the pheromones of two Phytocoris species in the western United States. The combination of hexyl, (E)-2-hexenyl, and (E)-2-octenyl acetates was as attractive to P. difficilis males as the milkweed bug pheromone, yet no milkweed bugs were drawn to this blend. Gas chromatographic (GC)-electroantennographic detection (EAD) and GC-mass spectrometric (MS) analyses of female P. difficilis MSGs determined that their secretion contained predominantly hexyl, (E)-2-hexenyl, and (E)-2-octenyl acetates (all strongly EAD-active)—the latter two compounds found only in trace amounts from males—plus five minor female-specific compounds, three of which were EAD-active. (E,E)-2,4-Hexadienyl acetate was not detected from P. difficilis females or males. The blend of the three major components, hexyl, (E)-2-hexenyl, and (E)-2-octenyl acetates (2:1.5:1 by volume), was as attractive as the blend of all six EAD-active compounds identified from females, indicating that this ternary blend constitutes the sex pheromone of P. difficilis. Hexyl acetate with (E)-2-octenyl acetate also attracted males of another species, P. breviusculus Reuter, but addition of (E)-2-hexenyl acetate and/or (E,E)-2,4-hexadienyl acetate inhibited attraction of P. breviusculus males. Attraction of P. difficilis males occurred mainly during the first half of scotophase. The possible neurophysiological basis for this asymmetrical cross-attraction is discussed.     

基于二次传播的开源软件缺陷定位方法

为了提高开源软件缺陷定位的准确度,提出一种基于二次传播的开源软件缺陷定位方法TPA。将重启随机游走算法与标签传播算法相结合,在基于信息检索方法的基础上,从开源软件中各个模块的调用关系、模块风险度和历史缺陷报告三个方面分析缺陷存在的具体位置,将缺陷定位分为风险度传播和标签传播两个部分。通过软件模块之间的相关度和调用关系,结合开源软件的历史修改信息评估模块风险度;通过已修改的缺陷报告构造缺陷标签,并采用标签传播定位软件缺陷。在Eclipse 3.1缺陷报告数据集中,top-◢N◣ rank和MRR指标均高于同类缺陷定位方法。实验表明,TPA方法具有较高的准确度。     

From source code to test cases: A comprehensive benchmark for resource leak detection in Android apps

Android apps share resources, such as sensors, cameras, and Global Positioning System, that are subject to specific usage policies whose correct implementation is left to programmers. Failing to satisfy these policies may cause resource leaks, that is, apps may acquire but never release resources. This might have different kinds of consequences, such as apps that are unable to use resources or resources that are unnecessarily active wasting battery. Researchers have proposed several techniques to detect and fix resource leaks. However, the unavailability of public benchmarks of faulty apps makes comparison between techniques difficult, if not impossible, and forces researchers to build their own data set to verify the effectiveness of their techniques (thus, making their work burdensome). The aim of our work is to define a public benchmark of Android apps affected by resource leaks. The resulting benchmark, called AppLeak, is publicly available on GitLab and includes faulty apps, versions with bug fixes (when available), test cases to automatically reproduce the leaks, and additional information that may help researchers in their tasks. Overall, the benchmark includes a body of 40 faults that can be exploited to evaluate and compare both static and dynamic analysis techniques for resource leak detection.     

基于代价敏感间隔分布优化的软件缺陷定位

在大型软件项目的开发与维护中,从大量的代码文件中定位软件缺陷费时、费力,有效地进行软件缺陷自动定位,将能极大地降低开发成本.软件缺陷报告通常包含了大量未发觉的软件缺陷的信息,精确地寻找与缺陷报告相关联的代码文件,对于降低维护成本具有重要意义.目前,已有一些基于深度神经网络的缺陷定位技术相对于传统方法,其效果有所提升,但相关工作大多关注网络结构的设计,缺乏对训练过程中损失函数的研究,而损失函数对于预测任务的性能会有极大的影响.在此背景下,提出了代价敏感的间隔分布优化（cost-sensitive margin distribution optimization,简称CSMDO）损失函数,并将代价敏感的间隔分布优化层应用到深度卷积神经网络中,能够良好地处理软件缺陷数据的不平衡性,进一步提高缺陷定位的准确度.     

基于Mozilla的安全性漏洞再修复经验研究

相较于其他类型的漏洞,安全性漏洞更容易发生再修复,这使得安全性漏洞需要更多的开发资源,从而增加了这些安全性漏洞修复的成本。因此,减少安全性漏洞再修复的发生的重要性不言而喻。对安全性漏洞再修复的经验研究有助于减少再修复的发生。首先,通过对Mozilla工程中一些发生再修复的安全性漏洞的安全性漏洞类型、发生再修复的原因、再修复的次数、修改的提交数、修改的文件数、修改的代码行数的增减、初始修复和再修复的对比等数据进行分析,发现了安全性漏洞发生再修复是普遍存在的,且与漏洞发生原因的识别的复杂程度和漏洞修复的复杂程度这两个因素有关;其次,初始修复涉及的文件、代码的集中程度是影响再修复的原因之一,而使用更复杂、更有效的修复过程可有效避免再修复的发生;最后,总结了几种安全性漏洞发生再修复的原因,使开发人员有效地识别不同类型的安全性漏洞再修复。     

基于本体的并发错误测试工具推荐方法研究

随着并发系统测试关注度的日益提高,越来越多的并发系统测试工具不断出现。对于测试人员来说,能否正确选取并发系统测试工具也就成为了并发测试效率的决定因素之一。鉴于并发错误检测软件并不像传统测试软件那样被人们所熟知,提出一种基于本体设计的并发错误测试工具推荐方法。该方法分别根据并发错误类型、程序本身特征和用户具体需求推荐适合的并发错误测试工具,从而提高测试的效率。     

面向软件安全性缺陷的开发者推荐方法

软件开发与维护过程中常会出现一些安全性缺陷,这些安全性缺陷会给软件和用户带来很大的风险.安全性缺陷在修复过程中,其修复级别和质量要求往往高于一般性的缺陷,因此,推荐出富有安全性经验的开发者及时有效地修复这些安全性缺陷非常重要.现有的开发者推荐技术在推荐开发者时仅仅考虑了开发者的历史开发内容,很少考虑到开发人员的安全性缺陷修复经验和修复质量等因素,所以这些技术不适用于安全性缺陷的开发者推荐.本文针对安全性缺陷的修复提出了一种有效的软件开发者推荐方法SecDR.SecDR在推荐开发者时不仅考虑了开发者的历史开发内容（与安全性相关）,还分析了开发者的修复质量和历史修复缺陷的复杂度等因素.此外,SecDR还实现了开发者的多经验级别推荐：推荐初级开发者修复简单的安全性缺陷,高级开发者修复复杂的安全性缺陷.本文在三个开源项目（Mozilla,Libgdx,ElasticSearch）上分别对SecDR推荐开发者进行有效性验证.通过对比实验证明,SecDR针对安全性缺陷推荐开发者相比于其他方法（如：DR_PSF）的推荐精度平均高出19%~42%.另外,实验对比了SecDR与实际开发人员的分配情况,结果显示SecDR可以更好地规避不合理的软件开发者的推荐.     

Experience report: investigating bug fixes in machine learning frameworks/libraries

Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.