Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity

Added value of varicocele surgery in the management of male infertility

Objectif : Evaluer l’impact du traitement chirurgical de la varicocèle sur les paramètres du spermogramme. Matériels et Méthodes : Notre étude a porté sur 127 hommes infertiles, ayant une varicocèle associée à des anomalies du spermogramme. Tous les patients ont subi un traitement chirurgical et un spermogramme a été demandé à 3 mois et 6 mois. Les données pré et post opératoires ont été comparées ainsi que le taux de grossesses spontanées évalué. Résultats : Après la chirurgie, nous notons une augmentation très discrète du pourcentage de spermatozoïdes des formes normales de 23,7 % à 27 %, de même que les concentrations moyenne de l’ensemble des patients allant de 10,9 x 10,6 /ml à 27,8 x10,6/ml ; et de 3,3 x 10,6/ml à 10,7 x10,6/ml chez les patients dont l’oligospermie avant chirurgie était sévère. La motilité moyenne des spermatozoïdes est passée de 25,1 % à 63,4 %, nous avons également considéré l’index d’anomalies multiples chez nos patients qui est passé de 1,72 à 1,21. La vitalité des spermatozoïdes est passée de 32,4% en moyenne à 71,6 %. Le taux de grossesses spontanées a été de 17 % au cours de la première année. Conclusion : En cas d’infertilité associée à une varicocèle cliniquement palpable, le traitement chirurgical de la varicocèle offre une excellente opportunité d’améliorer la qualité du spermogramme. Le nombre de grossesses spontanées observées après traitement doit inciter à proposer un traitement chirurgical d’une varicocèle, si celle-ci représente le seul élément étiologique de l’infertilité.Aim: Assess the impact of surgical treatment of varicocele on parameters of semen analysis. Material and Methods: Our study focused 127 infertile men with varicocele associated with abnormal semen analysis. All patients underwent surgical treatment and a semen analysis has been asked 3 months and 6 months after surgery. The preoperative and postoperative data were compared and the rate of spontaneous pregnancies evaluated. Results: After surgery, we noted an increase in the percentage of very discreet sperm normals forms of 23.7% to 27%, as average concentrations of all patients ranging from 10.9 X10.6 / ml to 27.8 X10.6 / ml and 3.3 x 10.6 / X10.6 to 10.7 ml / ml in patients with oligospermia was severe before surgery. The average sperm motility increased from 25.1% to 63.4%. We also considered the index of multiple anomalies in our patients, which rose from 1.72 to 1.21. The vitality of spermatozoa increased from 32.4% to 71.6% on average. The spontaneous pregnancy rate was 17% during the first year. Conclusion: In cases of infertility with a clinically palpable varicocele, surgical treatment is an excellent opportunity to improve the quality of sperm. The number of spontaneous pregnancies observed after treatment should encourage proposing a surgical treatment of varicocele, if it represents the single causative element of men infertility

Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques

Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.peerReviewe

Unraveling the Effect of MgAl/CuO Nanothermite on the Characteristics and Thermo-Catalytic Decomposition of Nanoenergetic Formulation Based on Nanostructured Nitrocellulose and Hydrazinium Nitro-Triazolone

The present study aims to develop new energetic composites containing nanostructured nitrocellulose (NNC) or nitrated cellulose (NC), hydrazinium nitro triazolone (HNTO), and MgAl-CuO nanothermite. The prepared energetic formulations (NC/HNTO/MgAl-CuO and NNC/HNTO/MgAl-CuO) were analyzed using various analytical techniques, such as Fourier-transform infrared (FTIR), scanning electron microscopy (SEM), thermogravimetry (TGA), and differential scanning calorimetry (DSC). The outstanding catalytic impact of MgAl-CuO on the thermal behavior of the developed energetic composites was elucidated by kinetic modeling, applied to the DSC data using isoconversional kinetic methods, for which a considerable drop in the activation energy was acquired for the prepared formulations, highlighting the catalytic influence of the introduced MgAl-CuO nanothermite. Overall, the obtained findings demonstrated that the newly elaborated NC/HNTO/MgAl-CuO and NNC/HNTO/MgAl-CuO composites could serve as promising candidates for application in the next generation of composite explosives and high-performance propellants

Elaboration, Characterization and Thermal Decomposition Kinetics of New Nanoenergetic Composite Based on Hydrazine 3-Nitro-1,2,4-triazol-5-one and Nanostructured Cellulose Nitrate

This research aims to develop new high-energy dense ordinary- and nano-energetic composites based on hydrazine 3-nitro-1,2,4-triazol-5-one (HNTO) and nitrated cellulose and nanostructured nitrocellulose (NC and NMCC). The elaborated energetic formulations (HNTO/NC and HNTO/NMCC) were fully characterized in terms of their chemical compatibility, morphology, thermal stability, and energetic performance. The experimental findings implied that the designed HNTO/NC and HNTO/NMCC formulations have good compatibilities with attractive characteristics such as density greater than 1.780 g/cm3 and impact sensitivity around 6 J. Furthermore, theoretical performance calculations (EXPLO5 V6.04) displayed that the optimal composition of the as-prepared energetic composites yielded excellent specific impulses and detonation velocities, which increased from 205.7 s and 7908 m/s for HNTO/NC to 209.6 s and 8064 m/s for HNTO/NMCC. Moreover, deep insight on the multi-step kinetic behaviors of the as-prepared formulations was provided based on the measured DSC data combined with isoconversional kinetic methods. It is revealed that both energetic composites undergo three consecutive exothermic events with satisfactory activation energies in the range of 139–166 kJ/mol for HNTO/NC and 119–134 kJ/mol for HNTO/NMCC. Overall, this research displayed that the new developed nanoenergetic composite based on nitrated cellulose nanostructure could serve as a promising candidate for practical applications in solid rocket propellants and composite explosives

Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues

The unprecedented growth of energy consumption in data centers created critical concern in recent years for both the research community and industry. Besides its direct associated cost; high energy consumption also results in a large amount of CO2 emission and incurs extra cooling expenditure. The foremost reason for overly energy consumption is the underutilization of data center resources. In modern data centers, virtualization provides a promising

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed