SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page

Supervised Contrastive Learning for Fine-grained Chromosome Recognition

Chromosome recognition is an essential task in karyotyping, which plays a vital role in birth defect diagnosis and biomedical research. However, existing classification methods face significant challenges due to the inter-class similarity and intra-class variation of chromosomes. To address this issue, we propose a supervised contrastive learning strategy that is tailored to train model-agnostic deep networks for reliable chromosome classification. This method enables extracting fine-grained chromosomal embeddings in latent space. These embeddings effectively expand inter-class boundaries and reduce intra-class variations, enhancing their distinctiveness in predicting chromosome types. On top of two large-scale chromosome datasets, we comprehensively validate the power of our contrastive learning strategy in boosting cutting-edge deep networks such as Transformers and ResNets. Extensive results demonstrate that it can significantly improve models' generalization performance, with an accuracy improvement up to +4.5%. Codes and pretrained models will be released upon acceptance of this work

Differences of Heart Rate Variability Between Happiness and Sadness Emotion States: A Pilot Study

This pilot study investigated the differences of heart rate variability (HRV) indices between two opposite emotion states: happiness and sadness, to reveal the differences of autonomic nervous system activity under different emotional states. Forty-eight healthy volunteers were enrolled for this study. Electrocardiography (ECG) signals were recorded under both emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). RR interval (RRI) time series were extracted from ECGs and multiple HRV indices, including time-domain (MEAN, SDNN, RMSSD and PNN50), frequency-domain (LFn, HFn and LF/HF) and nonlinear indices (SampEn and FuzzyMEn) were calculated. In addition, the effects of heart rate (HR) and mean artery pressure (MAP) on the aforementioned HRV indices were analyzed for both emotion states. The results showed that experimental order had no significant effect on all HRV indices from both happiness and sadness emotions (all P > 0.05). The key result was that among all nine HRV indices, six indices were identified having significant differences between happiness and sadness emotion states: MEAN (P = 0.028), SDNN (P = 0.002), three frequency-domain indices (all P < 0.0001) and FuzzyMEn (P = 0.047), whereas RMSSD, PNN50 and SampEn had no significant differences between the two emotion states. All indices, except for SampEn, had significant positive correlations (all P < 0.01) for the two emotion states. Four time-domain indices decreased with the increase of HR (all P < 0.01), while frequency-domain and nonlinear indices demonstrated no HR-related changes for each emotional state. In addition, all indices (time-domain, frequency-domain and nonlinear) showed no MAP-related changes. It concluded that HRV indices showed significant differences between happiness and sadness emotion states and the findings could help to better understand the inherent differences of cardiovascular time series between different emotion states in clinical practice

Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages

AimsThis study used entropy- and cross entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those entropy methods for identifying depression patients with different disease severities.MethodsElectrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS &gt;17). Sample entropy (SEn), fuzzy measure entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample entropy (CSEn) and cross fuzzy measure entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of entropy measures in this study.ResultsA consistent increasing trend has been found among most entropy measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p &lt; 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p &lt; 0.05), CSEn at 4 Hz (R = 0.26, p &lt; 0.05) or 2 Hz (R = 0.28, p &lt; 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p &lt; 0.01) or 2 Hz (R = 0.30, p &lt; 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between entropy measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition.ConclusionThe current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the entropy method

Down Syndrome detection with Swin Transformer architecture

Objective: Down Syndrome, also known as Trisomy 21, is a severe genetic disease caused by an extra chromosome 21. For the detection of Trisomy 21, despite those statistical methods have been widely used for screening, karyotyping remains the gold standard and the first level of testing for diagnosis. Due to karyotyping being a time-consuming and labour-intensive procedure, Computer Vision methodologies have been explored to automate the karyotyping process for decades. However, few studies have focused on Down Syndrome detection with the Transformer technique. This study develops a Down-Syndrome-Detector (DSD) architecture based on the Transformer structure, which includes a segmentation module, an alignment module, a classification module, and a Down Syndrome indicator. Methods: The segmentation and classification modules are designed by homogeneous transfer learning at the model level. Transfer learning techniques enable a network to share weights learned from the source domain (e.g., millions of data in ImageNet) and optimize the weights with limited labeled data in the target domain (e.g., less than 6,000 images in BioImLab). The Align-Module is designed to process the segmentation output to fit the classification dataset, and the Down Syndrome Indicator identifies a Down Syndrome case from the classification output. Results: Experiments are first performed on two public datasets BioImLab (119 cases) and Advanced Digital Imaging Research (ADIR, 180 cases). Our performance metrics indicate the good ability of segmentation and classification modules of DSD. Then, the DS detection performance of DSD is evaluated on a private dataset consisting of 1084 cells (including 20 DS cells from 2 singleton cases): 90.0% and 86.1% for cell-level TPR and TNR; 100% and 96.08% for case-level TPR and TNR, respectively. Conclusion: This study develops a pipeline based on the modern Transformer architecture for the detection of Down Syndrome from original metaphase micrographs. Both segmentation and classification models developed in this study are assessed using public datasets with commonly used metrics, and both achieved good results. The DSDproposed in this study reported satisfactory singleton case-specific DS detection results. Significance: As verified by a medical specialist, the developed method may improve Down Syndrome detection efficiency by saving human labor and improving clinical practice

Fully Automatic Karyotyping via Deep Convolutional Neural Networks

Chromosome karyotyping is an important yet labor-intensive procedure for diagnosing genetic diseases. Automating such a procedure drastically reduces the manual work of cytologists and increases congenital disease diagnosing precision. Researchers have contributed to chromosome segmentation and classification for decades. However, very few studies integrate the two tasks as a unified, fully automatic procedure or achieved a promising performance. This paper addresses the gap by presenting: 1) A novel chromosome segmentation module named ChrRender, with the idea of rendering the chromosome instances by combining rich global features from the backbone and coarse mask prediction from Mask R-CNN; 2) A devised chromosome classification module named ChrNet4 that pays more attention to channel-wise dependencies from aggregated informative features and calibrating the channel interdependence; 3) An integrated Render-Attention-Architecture to accomplish fully automatic karyotyping with segmentation and classification modules; 4) A strategy for eliminating differences between training data and segmentation output data to be classified. These proposed methods are implemented in three ways on the public Q-band BioImLab dataset and a G-band private dataset. The results indicate promising performance: 1) on the joint karyotyping task, which predicts a karyotype image by first segmenting an original microscopical image, then classifying each segmentation output with a precision of 89.75% and 94.22% on the BioImLab and private dataset, respectively; 2) On the separate task with two datasets, ChrRender obtained AP50 of 96.652% and 96.809% for segmentation, ChrNet4 achieved 95.24% and 94.07% for classification, respectively. The COCO format annotation files of BioImLab used in this paper are available at https://github.com/Alex17swim/BioImLab The study introduces an integrated workflow to predict a karyotyping image from a Microscopical Chromosome Image. With state-of-the-art performance on a public dataset, the proposed Render-Attention-Architecture has accomplished fully automatic chromosome karyotyping