Trade-Off Exploration for Acceleration of Continuous Integration

Continuous Integration (CI) is a popular software development practice that allows developers to quickly verify modifications to their projects. To cope with the ever-increasing demand for faster software releases, CI acceleration approaches have been proposed to expedite the feedback that CI provides. However, adoption of CI acceleration is not without cost. The trade-off in duration and trustworthiness of a CI acceleration approach determines the practicality of the CI acceleration process. Indeed, if a CI acceleration approach takes longer to prime than to run the accelerated build, the benefits of acceleration are unlikely to outweigh the costs. Moreover, CI acceleration techniques may mislabel change sets (e.g., a build labelled as failing that passes in an unaccelerated setting or vice versa) or produce results that are inconsistent with an unaccelerated build (e.g., the underlying reason for failure does not match with the unaccelerated build). These inconsistencies call into question the trustworthiness of CI acceleration products. We first evaluate the time trade-off of two CI acceleration products — one based on program analysis (PA) and the other on machine learning (ML). After replaying the CI process of 100,000 builds spanning ten open-source projects, we find that the priming costs (i.e., the extra time spent preparing for acceleration) of the program analysis product are substantially less than that of the machine learning product (e.g., average project-wise median cost difference of 148.25 percentage points). Furthermore, the program analysis product generally provides more time savings than the machine learning product (e.g., average project-wise median savings improvement of 5.03 percentage points). Given their deterministic nature, and our observations about priming costs and benefits, we recommend that organizations consider the adoption of program analysis based acceleration. Next, we study the trustworthiness of the same PA and ML CI acceleration products. We re-execute 50 failing builds from ten open-source projects in non-accelerated (baseline), program analysis accelerated, and machine learning accelerated settings. We find that when applied to known failing builds, program analysis accelerated builds more often (43.83 percentage point difference across ten projects) align with the non-accelerated build results. Accordingly, we conclude that while there is still room for improvement for both CI acceleration products, the selected program analysis product currently provides a more trustworthy signal of build outcomes than the machine learning product. Finally, we propose a mutation testing approach to systematically evaluate the trustworthiness of CI acceleration. We apply our approach to the deterministic PA-based CI acceleration product and uncover issues that hinder its trustworthiness. Our analysis consists of three parts: we first study how often the same build in accelerated and unaccelerated CI settings produce different mutation testing outcomes. We call mutants with different outcomes in the two settings “gap mutants”. Next, we study the code locations where gap mutants appear. Finally, we inspect gap mutants to understand why acceleration causes them to survive. Our analysis of ten thriving open-source projects uncovers 2,237 gap mutants. We find that: (1) the gap in mutation outcomes between accelerated and unaccelerated settings varies from 0.11%–23.50%; (2) 88.95% of gap mutants can be mapped to specific source code functions and classes using the dependency representation of the studied CI acceleration product; (3) 69% of gap mutants survive CI acceleration due to deterministic reasons that can be classified into six fault patterns. Our results show that deterministic CI acceleration suffers from trustworthiness limitations, and highlights the ways in which trustworthiness could be improved in a pragmatic manner. This thesis demonstrates that CI acceleration techniques, whether PA or ML-based, present time trade-offs and can reduce software build trustworthiness. Our findings lead us to encourage users of CI acceleration to carefully weigh both the time costs and trustworthiness of their chosen acceleration technique. This study also demonstrates that the following improvements for PA-based CI acceleration approaches would improve their trustworthiness: (1) depending on the size and complexity of the codebase, it may be necessary to manually refine the dependency graph, especially by concentrating on class properties, global variables, and constructor components; and (2) solutions should be added to detect and bypass flaky test during CI acceleration to minimize the impact of flakiness

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances

Effects of contusion load on cervical spinal cord:A finite element study

Injury of cervical spine is a common injury of locomotor system usually accompanied by spinal cord injury, however the injury mechanism of contusion load to the spinal cord is not clear. This study aims to investigate its injury mechanism associated with the contusion load, with different extents of spinal cord compression. A finite element model of cervical spinal cord was established and two scenarios of contusion injury loading conditions, i.e. back-to-front and front-to-back loads, were adopted. Four different compression displacements were applied to the middle section of the cervical spinal cord. The distributions of von Mises stress in middle transverse cross section were obtained from the finite element analysis. For the back-to-front loading scenario, the stress concentration was found in the area at and near the central canal and the damage may lead to the central canal syndrome from biomechanical point of view. With the front-to-back load, the maximum von Mises stress located in central canal area of gray matter when subject to 10% compression, whilst it appeared at the anterior horn when the compression increased. For the white matter, the maximum von Mises stress appeared in the area of the anterior funiculus. This leads to complicated symptoms given rise by damage to multiple locations in the cervical spinal cord. The illustrative results demonstrated the need of considering different loading scenarios in understanding the damage mechanisms of the cervical spinal cord, particularly when the loading conditions were given rise by different pathophysiological causes

When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks

In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial. In this paper, we find that ICL falls short of handling specification-heavy tasks, which are tasks with complicated and extensive task specifications, requiring several hours for ordinary humans to master, such as traditional information extraction tasks. The performance of ICL on these tasks mostly cannot reach half of the state-of-the-art results. To explore the reasons behind this failure, we conduct comprehensive experiments on 18 specification-heavy tasks with various LLMs and identify three primary reasons: inability to specifically understand context, misalignment in task schema comprehension with humans, and inadequate long-text understanding ability. Furthermore, we demonstrate that through fine-tuning, LLMs can achieve decent performance on these tasks, indicating that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback of existing alignment methods that renders LLMs incapable of handling complicated specification-heavy tasks via ICL. To substantiate this, we perform dedicated instruction tuning on LLMs for these tasks and observe a notable improvement. We hope the analyses in this paper could facilitate advancements in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie

Superior Facet Joint Violations during Single Level Minimally Invasive Transforaminal Lumbar Interbody Fusion: A Preliminary Retrospective Clinical Study

Background: Facet joint violation (FV) was reported as variable iatrogenic damage that can be a crucial risk factor leading to the adjacent segment degeneration (ASD). “Blind” screw placement technique in minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) contributes to the increasing incidence of FV that can be influenced by several potential factors. Many controversies about these factors and clinical outcomes of different types of FV patients exist, yet they have not been analyzed. Methods 99 cases undergoing single-segment MIS-TLIF from July 2013 to December 2015 were retrospectively analyzed. Computed tomography (CT) was applied to determine the incidence of FV, and then the correlation between FV and relevant factors, including gender, age, body mass index (BMI), top-screw level, and decompression, was analyzed. A total of 53 cases were followed up after one year, 31 cases in noninjury (A group) and 22 patients in FV injury (B group). Results The incidence of FV was 39. 39% (39/99) in the patients and 23.23% (46/198) in the screws. Logistic regression analysis showed that screw at L5 in patients with BMI > 30 kg/m2 was vulnerable to FV (P < 0.05). Moreover, postoperative average intervertebral disc height (AIDH) of fusion segment, visual analog scale (VAS), and Oswestry disability index (ODI) scores improved significantly in group A and B when compared with preoperative data (P < 0.05). Adjacent superior average intervertebral disc height (ASAIDH) presented decrease, but adjacent superior intervertebral disc Cobb angle (ASIDCA) appeared to increase in the two groups at the final follow-up compared with postoperative 3 days (P < 0.05). Low back VAS and ODI scores in group A (31 cases) were lower than those in group B (22 cases) in the final follow-up (P < 0.05). Conclusion MIS-TLIF is an effective treatment for lumbar degenerative disease, but FV occurred at a higher incidence. Facet joints should be protected in MIS-TLIF to avoid FV

Surface Immobilization of Redox-Labile Fluorescent Probes: Enabling Single-Cell Co-Profiling of Aerobic Glycolysis and Oncogenic Protein Signaling Activities.

An analytical method is described for profiling lactate production in single cells via the use of coupled enzyme reactions on surface-grafted resazurin molecules. The immobilization of the redox-labile probes was achieved through chemical modifications on resazurin, followed by bio-orthogonal click reactions. The lactate detection was demonstrated to be sensitive and specific. The method was incorporated into a single-cell barcode chip for simultaneous quantification of aerobic glycolysis activities and oncogenic signaling phosphoproteins in cancer. The interplay between glycolysis and oncogenic signaling activities was interrogated on a glioblastoma cell line. Results revealed a drug-induced oncogenic signaling reliance accompanying shifted metabolic paradigms. A drug combination that exploits this induced reliance exhibited synergistic effects in growth inhibition

Identification of potential drug targets for varicose veins: a Mendelian randomization analysis

IntroductionVaricose veins are a common chronic disease that creates a significant economic burden on the healthcare system. Current treatment options, including pharmacological treatments, are not always effective, and there is a need for more targeted therapies. A Mendelian randomization (MR) method uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, and it has been successful in identifying therapeutic targets in other diseases. However, few studies have used MR to explore potential protein drug targets for varicose veins.MethodsTo identify potential drug targets for varicose veins of lower extremities, we undertook a comprehensive screen of plasma protein with a two-sample MR method. We used recently reported cis-variants as genetic instruments of 2,004 plasma proteins, then applied MR to a recent meta-analysis of genome-wide association study on varicose veins (22,037 cases and 437,665 controls). Furthermore, pleiotropy detection, reverse causality testing, colocalization analysis, and external replication were utilized to strengthen the causal effects of prioritized proteins. Phenome-wide MR (PheW-MR) of the prioritized proteins for the risk of 525 diseases was conducted to screen potential side effects.ResultsWe identified eight plasma proteins that are significantly associated with the risk of varicose veins after Bonferroni correction (P &lt; 2.495 × 10−5), with five being protective (LUM, POSTN, RPN1, RSPO3, and VAT1) and three harmful (COLEC11, IRF3, and SARS2). Most identified proteins showed no pleiotropic effects except for COLLEC11. Bidirectional MR and MR Steiger testing excluded reverse causal relationship between varicose veins and prioritized proteins. The colocalization analysis indicated that COLEC11, IRF3, LUM, POSTN, RSPO3, and SARS2 shared the same causal variant with varicose veins. Finally, seven identified proteins replicated with alternative instruments except for VAT1. Furthermore, PheW-MR revealed that only IRF3 had potential harmful adverse side effects.ConclusionsWe identified eight potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that IRF3, LUM, POSTN, RSPO3, and SARS2 might be potential drug targets for varicose veins

Metabolomic analysis reveals spermatozoa and seminal plasma differences between Duroc and Liang guang Small-spotted pig

The Liang guang Small-spotted pig is a well-known Chinese indigenous pig that is valued for its exceptional meat quality. However, the Liang guang Small-spotted pig has a lower semen storage capacity, shorter storage time and worse semen quality compared to Duroc. Pig sperm used for artificial insemination (AI) loses part of vitality and quality when being stored in commercial solutions. Serious vitality losses and short shelf life of the semen are particularly prominent in Liang guang Small-spotted pig. In this study, the metabolites in seminal plasma and spermatozoa of Duroc and Liang guang Small-spotted pigs were identified using UHPLC–Q-TOF/MS technology. The findings indicated forty distinct metabolites concentrating on energy metabolic substrates and antioxidant capacity in Liang guang Small-spotted pig and Duroc seminal plasma, including D-Fructose, succinate, 2-dehydro-3-deoxy-d-gluconate, alanine betaine, citrate, carnitine, acetylcarnitine and so on. Seventeen different metabolites were explored, with a focus on glycerophospholipid metabolism in Liang guang Small-spotted pig and Duroc spermatozoa, primarily including glycerol 3-phosphate, acetylcarnitine, phosphatidylcholine (PC) 16:0/16:0, palmitoyl sphingomyelin, acetylcholine, choline, glycerophosphocholine, betaine, L-carnitine, creatinine and others. This study reveals the metabolite profile of spermatozoa and seminal plasma among different pig breeds and might be valuable for understanding the mechanisms that lead to sperm storage capacity. Metabolites involved in energy metabolism, antioxidant capacity and glycerophospholipid metabolism might be key to the poor sperm storage capacity in Liang guang Small-spotted pig

Global genetic differentiation of complex traits shaped by natural selection in humans

There are mean differences in complex traits among global human populations. We hypothesize that part of the phenotypic differentiation is due to natural selection. To address this hypothesis, we assess the differentiation in allele frequencies of trait-associated SNPs among African, Eastern Asian, and European populations for ten complex traits using data of large sample size (up to ~405,000). We show that SNPs associated with height ([Formula: see text]), waist-to-hip ratio ([Formula: see text]), and schizophrenia ([Formula: see text]) are significantly more differentiated among populations than matched "control" SNPs, suggesting that these trait-associated SNPs have undergone natural selection. We further find that SNPs associated with height ([Formula: see text]) and schizophrenia ([Formula: see text]) show significantly higher variance in linkage disequilibrium (LD) scores across populations than control SNPs. Our results support the hypothesis that natural selection has shaped the genetic differentiation of complex traits, such as height and schizophrenia, among worldwide populations

Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation (n = 1980) and epigenomic annotation data highlight 3 genes (CAMK1D, TP53INP1, and ATP5G1) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants.</p