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

Antiglycation, antiplatelets aggregation, cytotoxic and phytotoxic activities of Nepeta suavis

Nepeta suavis Stapf. (Lamiaceae), one of the ignored species for testing biological activities, was studied. In present research, the Nepeta suavis fractions: chloroform (FC), ethyl acetate (FE) and aqueous (FW) were evaluated for platelet aggregation, antiglycation, cytoxicity, and phytotoxicity. FE showed 65.60% antiglycation activity against the protein glycation while the other fractions showed less than 50% inhibitory potential. The FW inhibited arachidonic acid (AA) and platelet activating factor (acetyl-glyceryl-ether-phosphorylcholine, PAF) induced platelet aggregation. FE showed significant cytotoxicity against brine shrimp larvae with LD50 of 41.3 μg/ml. Phytotoxic studies of FC, FE and FW against Lemna minor showed 77.5-100% inhibitory effects at 1000 μg/ml. However, at lower concentration (10 μg/ml) enhancing effects were observed in FC and FE, as compared to control. FW remained in a uniform pattern of inhibitory effects in all three concentrations (10,100 and 1000 μg/ml). FE showed highest inhibitory activities against formation of glycation, while FW showed significant inhibitory effects against platelet aggregation and Lemna minor. Both of these fractions are recommended for further study to identify and isolate active chemical compounds.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Merocel Surgicel Wrap Technique to Manage Diffuse Epistaxis in Patients with Comorbidities

Epistaxis, or nasal bleeding, occurs in over half of the general population. It is caused by various etiological factors and affects both sexes and all age groups. The simplest treatment for a nosebleed is pinching of the ala nasi, referred to as the Hippocratic technique. In this study, we adopted different treatment protocols dependent on the severity of bleeding and assessed the etiology and efficacy of these modalities. This was a prospective study. We recruited 25 patients (24 adults and 1 child) who presented with epistaxis in the ENT departments of two tertiary care hospitals. We evaluated the cause of epistaxis and efficacy of the treatments used. All patients had experienced several episodes of epistaxis and were managed using anterior nasal packing with gauze and ointment or with Merocel packs alone. The incidence of epistaxis was more common in males than in females. It was effectively managed by anterior nasal packing with Surgicel-wrapped Merocel. Patients did not experience further episodes of bleeding following the removal of Merocel and retention of Surgicel in place

Performance Evaluation of Deep Learning Algorithm Using High-End Media Processing Board in Real-Time Environment

Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89

Pharmacological evaluation of methanolic extract of Cyperus scariosus

The present study is aimed to investigate the phytochemical screening and biological activities of methanolic extract of Cyperus scariosus roots. Dried plant was grounded and extracted with methanol to prepare methanol crud extract. In vitro biological tests were conducted using this methanolic extracts according to the standard procedure. 100% death rate of brine shrimp was perceived at 3 mg/mL of plant extract after 72 hours. The extract showed action against Aspergillus flavius i.e. 90% followed by A. niger (91%) while the highest activity was shown against A. fumegatrus (94%). Important scavenging results were detected during scavenging of free radicals viz; 92.2% against DPPH, 82.2% to ABTS, 75.8% to hydrogen peroxide, 88.1% to ?-carotene, 86.1% to hydroxyl radical and 89.4% against phosphomolybdate at 3 mg/mL were obtained. The results obtained in this study point out that extract showed significant biological activities which might be due to the presence of bioactive constituents

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

Box plot summary of development time (days) of different life stages.

<p>Minimum and maximum development time are shown by vertical lines, the upper and lower quartiles are shown by the bottom and top of box respectively, the median is represented by horizontal line inside box; where the median value is the same as the upper and lower quartile the top of the gray or the bottom of the white box represents the median. Individuals for which sex could not be determined due to death prior to adult emergence were excluded from this analysis, these unclassified individuals represented at most 43% of each type and averaged 26.6% (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058805#pone.0058805.s001" target="_blank">Table S1</a> for complete dataset. There was a significant difference in L4 larval development time between <i>Tx. amboinensis</i> and <i>Tx. splendens</i>.</p

Wing length results summarised in a box plot.

<p>Wing lengths for each adult are the average of the left and right wing measurements. Minimum and maximum wing lengths are shown by vertical lines, the upper and lower quartiles are shown by the bottom and top of box respectively, the median is represented by the horizontal line inside the box. Females that were fed on WT larvae (Control) were significantly smaller than females that were fed on RIDL larvae reared off-tetracycline (OFF-TET), highlighted by asterisk. No other significant differences were observed.</p