The Impact of Test Suite Granularity on the CostEffectiveness of Regression Testing

Regression testing is an expensive testing process used to validate software following modifications. The cost-effectiveness of regression testing techniques varies with characteristics of test suites. One such characteristic, test suite granularity, involves the way in which test inputs are grouped into test cases within a test suite. Various cost-benefits tradeoffs have been attributed to choices of test suite granularity, but almost no research has formally examined these tradeoffs. To address this lack, we conducted several controlled experiments, examining the effects of test suite granularity on the costs and benefits of several controlled experiments, examining the effects of test suite granularity on the costs and benefits of several regression testing methodologies across six releases of two non-trivial software systems. Our results expose essential tradeoffs to consider when designing test suites for use in regression testing evolving systems

On Test Suite Composition and Cost-Effective Regression Testing

Regression testing is an expensive testing process used to re-validate software as it evolves. Various methodologies for improving regression testing processes have been explored, but the cost-effectiveness of these methodologies has been shown to vary with characteristics of regression test suites. One such characteristic involves the way in which test inputs are composed into test cases within a test suite. This article reports the results..

The Impact of Test Suite Granularity on the Cost-Effectiveness of Regression Testing

Regression testing is an expensive testing process used to validate software following modi cations. The cost-eectiveness of regression testing techniques varies with characteristics of test suites. One suchcharacteristic, test suite granularity, involves the way in which test inputs are grouped into test cases within a test suite. Various cost-bene ts tradeos have been attributed to choices of test suite granularity, but almost no research has formally examined these tradeos. To address this lack, we conducted several controlled experiments, examining the eects of test suite granularity on the costs and bene ts of several regression testing methodologies across six releases of two non-trivial software systems. Our results expose essential tradeos to consider when designing test suites for use in regression testing evolving systems

Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Programme for providing innovative mental health care in rural communities in India.

BACKGROUND: India has few mental health professionals to treat the large number of people suffering from mental disorders. Rural areas are particularly disadvantaged due to lack of trained health workers. Ways to improve care could be by training village health workers in basic mental health care, and by using innovative methods of service delivery. The ongoing Systematic Medical Appraisal, Referral and Treatment Mental Health Programme will assess the acceptability, feasibility and preliminary effectiveness of a task-shifting mobile-based intervention using mixed methods, in rural Andhra Pradesh, India. METHOD: The key components of the study are an anti-stigma campaign followed by a mobile-based mental health services intervention. The study will be done across two sites in rural areas, with intervention periods of 1 year and 3 months, respectively. The programme uses a mobile-based clinical decision support tool to be used by non-physician health workers and primary care physicians to screen, diagnose and manage individuals suffering from depression, suicidal risk and emotional stress. The key aim of the study will be to assess any changes in mental health services use among those screened positive following the intervention. A number of other outcomes will also be assessed using mixed methods, specifically focussed on reduction of stigma, increase in mental health awareness and other process indicators. CONCLUSIONS: This project addresses a number of objectives as outlined in the Mental Health Action Plan of World Health Organization and India's National Mental Health Programme and Policy. If successful, the next phase will involve design and conduct of a cluster randomised controlled trial

Understanding the Effects of Changes on the Cost-Effectiveness of Regression Testing Techniques

Regression testing is an expensive testing process used to validate modified software. Regression test selection and test case prioritization can reduce the costs of regression testing by selecting a subset of test cases for execution, or scheduling test cases to better meet testing objectives. The cost-effectiveness of these techniques can vary widely, however, and one cause of this variance is the type and magnitude of changes made in producing a new software version. Engineers unaware of the causes and effects of this variance can make poor choices in designing change integration processes, selecting inappropriate regression testing techniques, designing excessively expensive regression test suites, and making unnecessarily costly changes. Engineers aware of causal factors can perform regression testing more cost-effectively. This paper reports..

Protocol for process evaluation of SMART Mental Health cluster randomised control trial:an intervention for management of common mental disorders in India

INTRODUCTION: In India about 95% of individuals who need treatment for common mental disorders like depression, stress and anxiety and substance use are unable to access care. Stigma associated with help seeking and lack of trained mental health professionals are important barriers in accessing mental healthcare. Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health integrates a community-level stigma reduction campaign and task sharing with the help of a mobile-enabled electronic decision support system (EDSS)—to reduce psychiatric morbidity due to stress, depression and self-harm in high-risk individuals. This paper presents and discusses the protocol for process evaluation of SMART Mental Health. METHODS AND ANALYSIS: The process evaluation will use mixed quantitative and qualitative methods to evaluate implementation fidelity and identify facilitators of and barriers to implementation of the intervention. Case studies of six intervention and two control clusters will be used. Quantitative data sources will include usage analytics extracted from the mHealth platform for the trial. Qualitative data sources will include focus group discussions and interviews with recruited participants, primary health centre doctors, community health workers (Accredited Social Health Activits) who participated in the project and local community leaders. The design and analysis will be guided by Medical Research Council framework for process evaluations, the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework, and the normalisation process theory. ETHICS AND DISSEMINATION: The study has been approved by the ethics committee of the George Institute for Global Health, India and the Institutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), New Delhi. Findings of the study will be disseminated through peer-reviewed publications, stakeholder meetings, digital and social media platforms. TRIAL REGISTRATION NUMBER: CTRI/2018/08/015355

An integrated community and primary healthcare worker intervention to reduce stigma and improve management of common mental disorders in rural India:protocol for the SMART Mental Health programme

Background: Around 1 in 7 people in India are impacted by mental illness. The treatment gap for people with mental disorders is as high as 75–95%. Health care systems, especially in rural regions in India, face substantial challenges to address these gaps in care, and innovative strategies are needed. Methods: We hypothesise that an intervention involving an anti-stigma campaign and a mobile-technology-based electronic decision support system will result in reduced stigma and improved mental health for adults at high risk of common mental disorders. It will be implemented as a parallel-group cluster randomised, controlled trial in 44 primary health centre clusters servicing 133 villages in rural Andhra Pradesh and Haryana. Adults aged ≥ 18 years will be screened for depression, anxiety and suicide based on Patient Health Questionnaire (PHQ-9) and Generalised Anxiety Disorders (GAD-7) scores. Two evaluation cohorts will be derived—a high-risk cohort with elevated PHQ-9, GAD-7 or suicide risk and a non-high-risk cohort comprising an equal number of people not at elevated risk based on these scores. Outcome analyses will be conducted blinded to intervention allocation. Expected outcomes: The primary study outcome is the difference in mean behaviour scores at 12 months in the combined ‘high-risk’ and ‘non-high-risk’ cohort and the mean difference in PHQ-9 scores at 12 months in the ‘high-risk’ cohort. Secondary outcomes include depression and anxiety remission rates in the high-risk cohort at 6 and 12 months, the proportion of high-risk individuals who have visited a doctor at least once in the previous 12 months, and change from baseline in mean stigma, mental health knowledge and attitude scores in the combined non-high-risk and high-risk cohort. Trial outcomes will be accompanied by detailed economic and process evaluations. Significance: The findings are likely to inform policy on a low-cost scalable solution to destigmatise common mental disorders and reduce the treatment gap for under-served populations in low-and middle-income country settings. Trial registration: Clinical Trial Registry India CTRI/2018/08/015355. Registered on 16 August 2018