LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models

Test suites tend to grow when software evolves, making it often infeasible to execute all test cases with the allocated testing budgets, especially for large software systems. Therefore, test suite minimization (TSM) is employed to improve the efficiency of software testing by removing redundant test cases, thus reducing testing time and resources, while maintaining the fault detection capability of the test suite. Most of the TSM approaches rely on code coverage (white-box) or model-based features, which are not always available for test engineers. Recent TSM approaches that rely only on test code (black-box) have been proposed, such as ATM and FAST-R. To address scalability, we propose LTM (Language model-based Test suite Minimization), a novel, scalable, and black-box similarity-based TSM approach based on large language models (LLMs). To support similarity measurement, we investigated three different pre-trained language models: CodeBERT, GraphCodeBERT, and UniXcoder, to extract embeddings of test code, on which we computed two similarity measures: Cosine Similarity and Euclidean Distance. Our goal is to find similarity measures that are not only computationally more efficient but can also better guide a Genetic Algorithm (GA), thus reducing the overall search time. Experimental results, under a 50% minimization budget, showed that the best configuration of LTM (using UniXcoder with Cosine similarity) outperformed the best two configurations of ATM in three key facets: (a) achieving a greater saving rate of testing time (40.38% versus 38.06%, on average); (b) attaining a significantly higher fault detection rate (0.84 versus 0.81, on average); and, more importantly, (c) minimizing test suites much faster (26.73 minutes versus 72.75 minutes, on average) in terms of both preparation time (up to two orders of magnitude faster) and search time (one order of magnitude faster)

Optimize along the way: An industrial case study on web performance

Context: Fast loading web apps can be a key success factor in terms of user experience. However, improving the performance of a web app is not trivial, since it requires a deep understanding of both the browser engine and the specific usage scenarios of the web app under consideration. Aims: In this paper, we present an industrial case study at 30 MHz, an agricultural technology company, in which we target a large web-based dashboard, where its performance was improved via 13 distinct interventions over a four-month period. Moreover, we conduct a user study to analyse whether web performance metrics correlate with the user perceived page load time in optimization scenarios. Methods: First, we design a replicable performance engineering plan, where the technical realization of each intervention is reported in detail along with its development effort. Second, we develop a benchmarking tool that supports 11 widely used web performance metrics. Finally, we use the benchmarking tool to quantitatively evaluate the performance of the target web app and measure the effect of 13 interventions on both desktop and mobile devices. For the user study, we record six videos of different page loads and ask participants about their opinion about the time a web page is considered ready. We calculate the correlation of the user perceived data with each web performance metric. Results: We observe a considerable performance improvement over the course of the 13 interventions. Among others, we achieve 98.37% and 97.56% time reductions on desktop and mobile, respectively, for the First Contentful Paint metric. In addition, we achieve 48.25% and 19.85% improvements for the Speed Index (SI) metric on desktop and mobile, respectively. Our user study shows that the Lowest Time to Widget metric, a product-specific web performance metric, is perfectly correlated with perceived performance during the optimization process. Conclusion: This study shows the importance of a continuous focus on performance engineering in the context of large-scale web apps to improve user browsing experience. We recommend developers to carefully plan their performance engineering activities, since different interventions might require different efforts and can have different effects on the overall performance of the web application

“Losing the Brakes”—Suppressed Inhibitors Triggering Uncontrolled <i>Wnt</i>/<i>ß-Catenin</i> Signaling May Provide a Potential Therapeutic Target in Elderly Acute Myeloid Leukemia

Dysregulated Wnt/β-catenin signal transduction is implicated in initiation, propagation, and poor prognosis in AML. Epigenetic inactivation is central to Wnt/β-catenin hyperactivity, and Wnt/β-catenin inhibitors are being investigated as targeted therapy. Dysregulated Wnt/β-catenin signaling has also been linked to accelerated aging. Since AML is a disease of old age (>60 yrs), we hypothesized age-related differential activity of Wnt/β-catenin signaling in AML patients. We probed Wnt/β-catenin expression in a series of AML in the elderly (>60 yrs) and compared it to a cohort of pediatric AML (n = 101) were evaluated for key Wnt/β-catenin molecule expression utilizing the NanoString platform. Differential expression of significance was defined as >2.5-fold difference (p 60 yrs) were identified in this cohort. Normal bone marrows (n = 10) were employed as controls. Wnt/β-catenin target genes (MYC, MYB, and RUNX1) showed upregulation, while Wnt/β-catenin inhibitors (CXXR, DKK1-4, SFRP1-4, SOST, and WIFI) were suppressed in elderly AML compared to pediatric AML and controls. Our data denote that suppressed inhibitor expression (through mutation or hypermethylation) is an additional contributing factor in Wnt/β-catenin hyperactivity in elderly AML, thus supporting Wnt/β-catenin inhibitors as potential targeted therapy

A fine-grained data set and analysis of tangling in bug fixing commits

Abstract Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objectives: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results: We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusions: Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise

Epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study

Purpose: In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods: We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results: 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions: HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes.</p

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42·4% vs 44·2%; absolute difference -1·69 [-9·58 to 6·11] p=0·67; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5-8] vs 6 [5-8] cm H2O; p=0·0011). ICU mortality was higher in MICs than in HICs (30·5% vs 19·9%; p=0·0004; adjusted effect 16·41% [95% CI 9·52-23·52]; p&lt;0·0001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0·80 [95% CI 0·75-0·86]; p&lt;0·0001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status

Epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study

Purpose In the critically ill, hospital-acquired bloodstream infections (HA-BSI) are associated with significant mortality. Granular data are required for optimizing management, and developing guidelines and clinical trials. Methods We carried out a prospective international cohort study of adult patients (≥ 18 years of age) with HA-BSI treated in intensive care units (ICUs) between June 2019 and February 2021. Results 2600 patients from 333 ICUs in 52 countries were included. 78% HA-BSI were ICU-acquired. Median Sequential Organ Failure Assessment (SOFA) score was 8 [IQR 5; 11] at HA-BSI diagnosis. Most frequent sources of infection included pneumonia (26.7%) and intravascular catheters (26.4%). Most frequent pathogens were Gram-negative bacteria (59.0%), predominantly Klebsiella spp. (27.9%), Acinetobacter spp. (20.3%), Escherichia coli (15.8%), and Pseudomonas spp. (14.3%). Carbapenem resistance was present in 37.8%, 84.6%, 7.4%, and 33.2%, respectively. Difficult-to-treat resistance (DTR) was present in 23.5% and pan-drug resistance in 1.5%. Antimicrobial therapy was deemed adequate within 24 h for 51.5%. Antimicrobial resistance was associated with longer delays to adequate antimicrobial therapy. Source control was needed in 52.5% but not achieved in 18.2%. Mortality was 37.1%, and only 16.1% had been discharged alive from hospital by day-28. Conclusions HA-BSI was frequently caused by Gram-negative, carbapenem-resistant and DTR pathogens. Antimicrobial resistance led to delays in adequate antimicrobial therapy. Mortality was high, and at day-28 only a minority of the patients were discharged alive from the hospital. Prevention of antimicrobial resistance and focusing on adequate antimicrobial therapy and source control are important to optimize patient management and outcomes