A survey on test suite reduction frameworks and tools

Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks

IEEE Access Special section Editorial: Mobile edge computing and mobile cloud computing: Addressing heterogeneity and energy issues of compute and network resources

[No abstract available

Mitral regurgitation as a phenotypic manifestation of nonphotosensitive trichothiodystrophy due to a splice variant in MPLKIP

Background: Nonphotosensitive trichothiodystrophy (TTDN) is a rare autosomal recessive disorder of neuroectodermal origin. The condition is marked by hair abnormalities, intellectual impairment, nail dystrophies and susceptibility to infections but with no UV sensitivity. Methods: We identified three consanguineous Pakistani families with varied TTDN features and used homozygosity mapping, linkage analysis, and Sanger and exome sequencing in order to identify pathogenic variants. Haplotype analysis was performed and haplotype age estimated. A splicing assay was used to validate the effect of the MPLKIP splice variant on expression. Results: Affected individuals from all families exhibit several TTDN features along with a heart-specific feature, i.e. mitral regurgitation. Exome sequencing in the probands from families ED168 and ED241 identified a homozygous splice mutation c.339 + 1G > A within MPLKIP. The same splice variant co-segregates with TTDN in a third family ED210. The MPLKIP splice variant was not found in public databases, e.g. the Exome Aggregation Consortium, and in unrelated Pakistani controls. Functional analysis of the splice variant confirmed intron retention, which leads to protein truncation and loss of a phosphorylation site. Haplotype analysis identified a 585.1-kb haplotype which includes the MPLKIP variant, supporting the existence of a founder haplotype that is estimated to be 25,900 years old. Conclusion: This study extends the allelic and phenotypic spectra of MPLKIP-related TTDN, to include a splice variant that causes cardiomyopathy as part of the TTDN phenotype

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Lightweight energy estimation framework for smart-phone applications using static analysis / Raja Wasim Ahmad

Recently, the preference of users has shifted the computational platform to resource constrained smart-phone devices as users prefer to work while on the go. The shift of information access paradigm on smart-phone devices demand high functionality applications to enrich user experience. However, increasing applications functionality requires more smart-phone resources. As a result, smart-phone battery consumption increases. Smart-phone application energy estimation investigates energy consumption behavior of smart-phone applications at diversified granularity levels when it is run on the smart-phone device. Traditional energy estimation schemes consider smart-phone component’s power measurement or code analysis methods for energy estimation of smart-phone applications. Code analysis based methods use energy cost of operations within an application to estimate energy consumption. However, smart-phone applications are non-deterministic in nature. Therefore, traditional code analysis based energy estimation schemes run the smart-phone application to record the execution paths in offline mode to estimate its energy consumption. However, running application on hardware platform inefficiently utilizes underlying hardware resources that lead to extended estimation time and energy estimation overhead. To overcome this issue, this study proposes a lightweight 2-tier static analysis based energy estimation framework to minimize high energy overhead of dynamic analysis based energy estimation methods. The proposed framework, called Static analysis based lightweight energy estimation framework (SA-LEEF), proposes storage location analyzer, ARM-IS energy profile as service, and weighted probability based execution paths estimation to handle non-deterministic nature of smart-phone applications. Moreover, the proposed framework considers the energy overhead due to cache eviction during concurrent programs execution on the smart phone device to present more realistic application execution environment for energy estimation. It also considers user system interaction to input required data during application execution on the smart-phone device to improve the energy estimation accuracy. The proposed framework empowers application developers to estimate energy consumption at source code line, functions, execution paths, and application granularity. The proposed study has performed experiments on Google Nexus One smart-phone device to highlight the effectiveness of SA-LEEF framework. The experiments revealed that SA-LEEF has minimized energy estimation time of dynamic analysis methods by 98% for benchmark applications. In terms of energy overhead, SA-LEEF consumes up to 97% less energy than dynamic analysis based energy estimation method. The accuracy of SA-LEEF is up to 88% compared to external physical measurement method. It is also noticed that SA-LEEF consumes 58% less CPU and 97% lower RAM storage during energy estimation of a smart-phone application. SA-LEEF assist developers investigating energy consumption behavior of their application at earlier development stages as it estimates energy consumption based on fine granular instruction energy cost

Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies

The year 2020 has witnessed unprecedented levels of demand for COVID-19 medical equipment and supplies. However, most of today's systems, methods, and technologies leveraged for handling the forward supply chain of COVID-19 medical equipment and the waste that results from them after usage are inefficient. They fall short in providing traceability, reliability, operational transparency, security, and trust features. Also, they are centralized that can cause a single point of failure problem. In this paper, we propose a decentralized blockchain-based solution to automate forward supply chain processes for the COVID-19 medical equipment and enable information exchange among all the stakeholders involved in their waste management in a manner that is fully secure, transparent, traceable, and trustworthy. We integrate the Ethereum blockchain with decentralized storage of interplanetary file systems (IPFS) to securely fetch, store, and share the data related to the forward supply chain of COVID-19 medical equipment and their waste management. We develop algorithms to define interaction rules regarding COVID-19 waste handling and penalties to be imposed on the stakeholders in case of violations. We present system design along with its full implementation details. We evaluate the performance of the proposed solution using cost analysis to show its affordability. We present the security analysis to verify the reliability of the smart contracts, and discuss our solution from the generalization and applicability point of view. Furthermore, we outline the limitations of our solution in form of open challenges that can act as future research directions. We make our smart contracts code publicly available on GitHub

Extended Survivable Passive Access Network (e-SPAN)

Wavelength Division Multiplexed based Passive Optical Networks (WDM-PON) are subjected to wide variety of incidental failures. It is preferred in WDM-PONs to provide fault management (survivability) at link layer level. In this paper, our objective is to determine the factors, which can increase the scope of survivability at link layer, not available currently. We propose a cost-effective recovery mechanism, called ldquoExtended Survivable Passive Access Networkrdquo (e-SPAN). e SPAN is a multi-level protection scheme that has potential to corrects eventual errors that are caused by the transmission system by using different forward error correcting codes at receiver end, thus allowing us to have better performance budget in terms of BER and extended transmission distance of 8 to 12 Km