Test Case Selection in Continuous Integration Using Reinforcement Learning with Linear Function Approximator

Continuous Integration (CI) has become an essential practice in software development, allowing teams to integrate code changes frequently and detect issues early. However, the selection of proper test cases for CI remains a challenge, as it requires balancing the need for thorough testing with the minimization of execution time and resources. This study proposes a practical and lightweight approach that leverages Reinforcement Learning with a linear function approximator for test case selection in CI. Several models are created where each one focuses on a different feature set. The proposed method aims to optimize the selection of test cases by learning from past CI outcomes, both the historical data of the test cases and the coverage data of the source code, and dynamically adapting the models for encountering new test cases and modified source code. Through experimentation and comparison between the models, the study demonstrates which feature set is optimal and efficient. The result indicates that Reinforcement Learning with a linear function approximator using coverage information can effectively assist in selecting test cases in CI, leading to enhanced software quality and development efficiency

Test Case Selection in Continuous Integration Using Reinforcement Learning with Linear Function Approximator

Continuous Integration (CI) has become an essential practice in software development, allowing teams to integrate code changes frequently and detect issues early. However, the selection of proper test cases for CI remains a challenge, as it requires balancing the need for thorough testing with the minimization of execution time and resources. This study proposes a practical and lightweight approach that leverages Reinforcement Learning with a linear function approximator for test case selection in CI. Several models are created where each one focuses on a different feature set. The proposed method aims to optimize the selection of test cases by learning from past CI outcomes, both the historical data of the test cases and the coverage data of the source code, and dynamically adapting the models for encountering new test cases and modified source code. Through experimentation and comparison between the models, the study demonstrates which feature set is optimal and efficient. The result indicates that Reinforcement Learning with a linear function approximator using coverage information can effectively assist in selecting test cases in CI, leading to enhanced software quality and development efficiency

Epidemiologic study of chronic hepatitis B virus infection in male volunteer blood donors in Karachi, Pakistan

Abstract Background The magnitude of chronic infection with hepatitis B virus (HBV) varies substantially between the countries. A better understanding of incidence and/ or prevalence of HBV infection and associated risk factors provides insight into the transmission of this infection in the community. The purpose of this investigation was to estimate the prevalence of and to identify the risk factors associated with chronic infection with HBV, as assessed by HBV surface antigen (HBsAg) positivity, in asymptomatic volunteer male blood donors in Karachi, Pakistan. Methods Consecutive blood donations made at the two large blood banks between January 1, 1998 and December 31, 2002 were assessed to estimate the prevalence of HBsAg positivity. To evaluate the potential risk factors, a case-control study design was implemented; cases (HBsAg positives) and controls (HBsAg negatives), were recruited between October 15, 2001 and March 15, 2002. A pre-tested structured questionnaire was administered through trained interviewers to collect the data on hypothesized risk factors for HBV infection. Sera were tested for HBsAg using commercially available kits for enzyme linked Immunosorbant assay-III. Results HBsAg prevalence in the male volunteer blood donors was 2.0 % (7048/351309). Multivariate logistic regression analysis showed that after adjusting for age and ethnicity, cases were significantly more likely than controls to have received dental treatment from un-qualified dental care provider (adjusted odds ratio (OR) = 9.8; 95% confidence interval (CI): 2.1, 46.1), have received 1–5 injections (adjusted OR = 3.3; 95% CI: 1.1, 9.6), more than 5 injections (adjusted OR = 1.4; 95% CI: 1.4, 12.7) during the last five years or have received injection through a glass syringe (adjusted OR = 9.4; 95% CI: 2.6, 34.3). Injury resulted in bleeding during shaving from barbers (adjusted OR = 2.3; 95% CI: 1.1, 4.8) was also significant predictor of HBsAg positivity. Conclusion Prevalence of HBsAg positivity in the male volunteer blood donors in Karachi was 2%. Infection control measures in health-care settings including safe injection practices and proper sterilization techniques of medical instruments and education of barbers about the significance of sterilization of their instruments may reduce the burden of HBV infection in this and similar settings. There is also an urgent need of developing locally relevant guidelines for counseling and management of HBsAg positive blood donors.</p

Construction of database and tools for collection and handling of data from evidence instruments

Denna rapport behandlar ett kandidatprojekt som utförts med Nationellt forensiskt centrum som kund. Uppgiften var att skapa ett databasbaserat program för att lagra och hantera stora mängder XML-filer från kundens testinstrument. Rapporten beskriver de tekniker och metoder som använts för detta ändamål, där Java, MySQL, Git och agil utveckling är några av de mest centrala. Resultaten i projektet innefattar en presentation av slutprodukten, projektgruppens erfarenheter och medlemmarnas individuella bidrag. Utöver den gemensamma kandidatrapporten, har varje gruppmedlem genomfört ett individuellt bidrag om valfritt ämne, ofta kopplat till medlemmens projektroll. Detta gav möjlighet till fördjupning inom områden som medlemmarna fann intressanta. Utifrån diskussionen av dessa resultat dras, bland flera, slutsatserna att det utvecklade projektet kommer att bidra till tidsbesparingar för kunden, att en systemanatomi är ett användbart kommunikationsverktyg i början av ett projekt och att utvecklingen begränsades av valet av teknologier

A Comparative Study of the Treatment Efficiency of Floating and Constructed Wetlands for the Bioremediation of Phenanthrene-Contaminated Water

Employing floating treatment wetlands (FTWs) and constructed wetlands (CWs) is one of the most eco-friendly strategies for the bioremediation of water contaminants. Here, the efficiency of FTWs and CWs was compared for the degradation of phenanthrene-contaminated water for the first time. The FTWs and CWs were established by vegetated Phragmites australis in phenanthrene (1000 mg L−1)-contaminated water. Both wetlands were augmented with a bacterial consortium of four bacterial strains: Burkholderia phytofirmans PsJN, Pseudomonas anguiliseptica ITRI53, Arthrobacter oxydans ITRH49, and Achromobacter xylosoxidans ITSI70. Overall, the wetlands removed 91–93% of the phenanthrene whilst the augmentation of the bacterial strains had a synergistic effect. In comparison, the CWs showed a better treatment efficiency, with a 93% reduction in phenanthrene, a 91.7% reduction in the chemical oxygen demand, an 89% reduction in the biochemical oxygen demand, and a 100% reduction in toxicity. The inoculated bacteria were found growing in the shoots, roots, and water of both wetlands, but were comparatively better adapted to the CWs when compared with the FTWs. Similarly, the plants vegetated in the CWs exhibited better growth than that observed in the FTWs. This study revealed that the FTWs and CWs vegetated with P. australis both had promising potential for the cost-effective bioremediation of phenanthrene-contaminated water

A Comparative Study of the Treatment Efficiency of Floating and Constructed Wetlands for the Bioremediation of Phenanthrene-Contaminated Water

Employing floating treatment wetlands (FTWs) and constructed wetlands (CWs) is one of the most eco-friendly strategies for the bioremediation of water contaminants. Here, the efficiency of FTWs and CWs was compared for the degradation of phenanthrene-contaminated water for the first time. The FTWs and CWs were established by vegetated Phragmites australis in phenanthrene (1000 mg L&minus;1)-contaminated water. Both wetlands were augmented with a bacterial consortium of four bacterial strains: Burkholderia phytofirmans PsJN, Pseudomonas anguiliseptica ITRI53, Arthrobacter oxydans ITRH49, and Achromobacter xylosoxidans ITSI70. Overall, the wetlands removed 91&ndash;93% of the phenanthrene whilst the augmentation of the bacterial strains had a synergistic effect. In comparison, the CWs showed a better treatment efficiency, with a 93% reduction in phenanthrene, a 91.7% reduction in the chemical oxygen demand, an 89% reduction in the biochemical oxygen demand, and a 100% reduction in toxicity. The inoculated bacteria were found growing in the shoots, roots, and water of both wetlands, but were comparatively better adapted to the CWs when compared with the FTWs. Similarly, the plants vegetated in the CWs exhibited better growth than that observed in the FTWs. This study revealed that the FTWs and CWs vegetated with P. australis both had promising potential for the cost-effective bioremediation of phenanthrene-contaminated water