Issue Resolution Time Prediction Using Deep Learning Techniques

Probleemi lahendamise ajakulu prognoosimine tarkvaraprojektide korral on suure tähtsusega, kuna selliste projektide planeerimine on raske. Probleemi lahendamisele kuluva aja täpne hindamine on eriti vajalik agiilses tarkvaraarenduses, nt sprindi planeerimises, sest see võimaldab planeerida täpselt. Käesolev magistritöö keskendub antud probleemi kaasaegsetele lähenemisviisidele ja nende efektiivsuse uurimisele. Töös arutletakse selle üle, kuidas on võimalik struktureerida ja rakendada süvaõppe algoritme probleemi lahendamisele kuluva aja prognoosimiseks. Süvaõppe meetodite abil saadud tulemusi võrreldakse teiste kaasaegsete tulemustega. Andmed, mida antud lõputöös kasutatakse, sisaldavad umbes 700 000 ülesannet. Andmed on kollektiivselt kogutud samas valdkonnas varasemalt läbiviidud uurimustest. Kasutades olemasolevaid andmeid, on töös plaanis valideerida olemasolevaid tulemusi ja neid täiendada.Issue resolution time prediction has a large importance in software projects since planning of these projects are typically hard. Especially in the agile practices, such as sprint planning, to be able to predict correctly how long it would take to resolve an issue, holds the power to plan correctly. This thesis focuses on the state of the art approaches to this problem and study their performances. On top of that we discuss how can one structure and implement a deep learning algorithm to solve issue resolution time prediction problem. Afterwards, we compare and discuss the results of the applied deep learning technique with the current state of the art.The data used for this study contains around 700,000 issues. This data is gathered collectively from the previous studies in this field. By using the already existing data, we plan to validate the existing results and build on top of the current baseline

Intravenous paracetamol versus dexketoprofen in acute musculoskeletal trauma in the emergency department: A randomised clinical trial

Introduction: Musculoskeletal system traumas are among the most common presentations in the emergency departments. In the treatment of traumatic musculoskeletal pain, paracetamol and non-steroidal anti-inflammatory analgesics (NSAID) are frequently used. Our aim in this study is to compare the efficacy of intravenous dexketoprofen and paracetamol in the treatment of traumatic musculoskeletal pain. Methods: This prospective, randomised, double blind, controlled study was conducted in a tertiary care emergency unit. The participating patients were randomised into two groups to receive either 50 mg of dexketoprofen or 1000 mg of paracetamol intravenously by rapid infusion in 150 mL of normal saline. Visual analogue scale (VAS), Numeric Rating Scala (NRS) and Verbal Rating Scale (VRS) was employed for pain measurement at baseline, after 15, after 30 and after 60 mins. Results: 200 patients were included in the final analysis. The median age of the paracetamol group was 34 (24–48), while that of the dexketoprofen group was 35 (23–50), and 63% (n = 126) of them consisted of men. Paracetamol and dexketoprofen administration reduced VAS pain scores over time (p = 0.0001). Median reduction in VAS score at 60 min was 55 (IQR 30–65) for the paracetamol group and 50(IQR 30.25–60) for the dexketoprofen group. There was no statistically significant difference between the paracetamol and dexketoprofen groups in terms of VAS reductions (p = 0.613). Conclusion: Intravenous paracetamol and dexketoprofen seem to produce equivalent pain relief for acute musculoskeletal trauma in the emergency department. CLINICALTRIALS.GOV NO: NCT03428503 © 2018 Elsevier Inc