Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

2021 ◽  
pp. 762-781
Author(s):  
Miloud Dahane ◽  
Mustapha Kamel Abdi ◽  
Mourad Bouneffa ◽  
Adeel Ahmad ◽  
Henri Basson
Keyword(s):  
Statistical Analysis ◽  
Side Effects ◽  
Design Of Experiments ◽  
Software Maintenance ◽  
Software Metrics ◽  
Software Evolution ◽  
Measurement Tool ◽  
Software Artifacts ◽  
Change Impact ◽  
Evolution Control

Software evolution control mostly relies on the better structure of the inherent software artifacts and the evaluation of different qualitative factors like maintainability. The attributes of changeability are commonly used to measure the capability of the software to change with minimal side effects. This article describes the use of the design of experiments method to evaluate the influence of variations of software metrics on the change impact in developed software. The coupling metrics are considered to analyze their degree of contribution to cause a change impact. The data from participant software metrics are expressed in the form of mathematical models. These models are then validated on different versions of software to estimate the correlation of coupling metrics with the change impact. The proposed approach is evaluated with the help of a set of experiences which are conducted using statistical analysis tools. It may serve as a measurement tool to qualify the significant indicators that can be included in a Software Maintenance dashboard.

Using Design of Experiments to Analyze Open Source Software Metrics for Change Impact Estimation

2019 ◽  
Vol 10 (1) ◽  
pp. 16-33
Author(s):  
Miloud Dahane ◽  
Mustapha Kamel Abdi ◽  
Mourad Bouneffa ◽  
Adeel Ahmad ◽  
Henri Basson
Keyword(s):  
Statistical Analysis ◽  
Design Of Experiments ◽  
Open Source Software ◽  
Software Maintenance ◽  
Software Metrics ◽  
Software Evolution ◽  
Measurement Tool ◽  
Software Artifacts ◽  
Change Impact ◽  
Evolution Control

Software evolution control mostly relies on the better structure of the inherent software artifacts and the evaluation of different qualitative factors like maintainability. The attributes of changeability are commonly used to measure the capability of the software to change with minimal side effects. This article describes the use of the design of experiments method to evaluate the influence of variations of software metrics on the change impact in developed software. The coupling metrics are considered to analyze their degree of contribution to cause a change impact. The data from participant software metrics are expressed in the form of mathematical models. These models are then validated on different versions of software to estimate the correlation of coupling metrics with the change impact. The proposed approach is evaluated with the help of a set of experiences which are conducted using statistical analysis tools. It may serve as a measurement tool to qualify the significant indicators that can be included in a Software Maintenance dashboard.

scholarly journals Determination of Platelets Parameters among People Vaccinated with Oxford-AstraZeneca Vaccine - COVID Vaccine at Khartoum State -Sudan

2021 ◽  
pp. 529-535
Author(s):  
Wafa Salah Eldein Ibrahim Mohamed ◽  
Elharam Ibrahim Abdallah ◽  
Alaa Eltayeb Omer ◽  
Lienda Bashier Eltayeb
Keyword(s):  
Statistical Analysis ◽  
Side Effects ◽  
Portal Vein ◽  
Portal Vein Thrombosis ◽  
Muscle Pain ◽  
Healthcare Providers ◽  
Vein Thrombosis ◽  
Significant Difference ◽  
And Gender

Background: The global SARS-CoV-2 vaccination program has been hampered by the rare-and initially inexplicable emergence of vaccine-associated thrombosis, particularly venous territory strokes or other venous obstructions, including portal vein thrombosis, which has been dubbed Vaccine-Induced Thrombotic Thrombocytopenia (VITT). So, this study was conducted to determine platelets parameters among people vaccinated with the AstraZeneca vaccine at Khartoum state. Materials & Methods: A total of 50 AstraZeneca vaccinated participants (22 male and 26 female) were utilized as a case and 50 healthy non-vaccinated participants (21 male and 29 female) were used as control. The age of both groups ranged between (20-62) years with a mean of 34.6 ± 11.9. Platelets parameters were assayed for all patients using Sysmex KX-21. Results: The statistical analysis was performed by using SPSS. The results of the study showed that there was no significant difference in platelets count and platelets indices when compared according to vaccine intake and gender. Also, the most frequent symptoms among vaccinated people were: muscle pain at the site of puncture (56%), fatigue (54%), fever (34%), headache (22%), nausea (16%), and diarrhea (6%) respectively and developed no symptoms (30%). Conclusions: The study concludes that the side effects of the COVID-19 AstraZeneca vaccine in Khartoum state, Sudan was consistent with the manufacturers’ data.  Healthcare providers and recipients of vaccines can be more confident about the safety of Oxford-AstraZeneca COVID-19 vaccines.

scholarly journals Design of testing framework for code smell detection (OOPS) using BFO algorithm

2018 ◽  
Vol 7 (2.27) ◽  
pp. 161
Author(s):  
Pratiksha Sharma ◽  
Er. Arshpreet Kaur
Keyword(s):  
Software Metrics ◽  
Software Evolution ◽  
False Positive Rate ◽  
Optimization Technique ◽  
Research Work ◽  
Object Oriented Programming ◽  
Detection Methods ◽  
Major Drawback ◽  
Code Refactoring ◽  
Code Smell

Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.    

Applicability of Lehman Laws on Open Source Evolution

2015 ◽  
pp. 199-224
Author(s):  
Nisha Ratti ◽  
Parminder Kaur
Keyword(s):  
Open Source ◽  
Real World ◽  
Software Maintenance ◽  
Software Evolution ◽  
Object Oriented ◽  
User Requirements ◽  
Continuous Change ◽  
Essential Characteristic ◽  
Time Period ◽  
Object Oriented Metrics

Software evolution is the essential characteristic of the real world software as the user requirements changes software needs to change otherwise it becomes less useful. In order to be used for longer time period, software needs to evolve. The software evolution can be a result of software maintenance. In this chapter, a study has been conducted on 10 versions of GLE (Graphics Layout Engine) and FGS (Flight Gear Simulator) evolved over the period of eight years. An effort is made to find the applicability of Lehman Laws on different releases of two softwares developed in C++ using Object Oriented metrics. The laws of continuous change, growth and complexity are found applicable according to data collected.

Ontology Augmented Software Engineering

2013 ◽  
pp. 406-413
Author(s):  
Qazi Mudassar Ilyas
Keyword(s):  
Software Engineering ◽  
Semantic Web ◽  
System Integration ◽  
Software Maintenance ◽  
Domain Knowledge ◽  
System Development ◽  
Tracking System ◽  
Software Artifacts ◽  
Natural Choice ◽  
Development Life Cycle

Semantic Web was proposed to make the content machine-understandable by developing ontologies to capture domain knowledge and annotating content with this domain knowledge. Although, the original idea of semantic web was to make content on the World Wide Web machine-understandable, with recent advancements and awareness about these technologies, researchers have applied ontologies in many interesting domains. Many phases in software engineering are dependent on availability of knowledge, and the use of ontologies to capture and process this knowledge is a natural choice. This chapter discusses how ontologies can be used in various stages of the system development life cycle. Ontologies can be used to support requirements engineering phase in identifying and fixing inconsistent, incomplete, and ambiguous requirement. They can also be used to model the requirements and assist in requirements management and validation. During software design and development stages, ontologies can help software engineers in finding suitable components, managing documentation of APIs, and coding support. Ontologies can help in system integration and evolution process by aligning various databases with the help of ontologies capturing knowledge about database schema and aligning them with concepts in ontology. Ontologies can also be used in software maintenance by developing a bug tracking system based upon ontological knowledge of software artifacts and roles of developers involved in software maintenance task.

Synthesis of Silver Nanostructures by the Polyol Method and their Statistical Analysis Using Design of Experiments

2012 ◽  
Vol 1371 ◽  
Author(s):  
Jaime E. Pérez ◽  
Adriana B. Arauz ◽  
Luis A. García ◽  
José L. Rodríguez
Keyword(s):  
Statistical Analysis ◽  
Design Of Experiments ◽  
Polyol Process ◽  
Polyol Method ◽  
Synthesis Process ◽  
Silver Nanostructures ◽  
Significant Information ◽  
Injection Speed ◽  
Concentration Time ◽  
Time Of Reaction

ABSTRACTIn this work we present the bases to perform investigation on the effects on the morphology and size of nanostructures of silver, owed to the modification of synthesis factors in a polyol process such as temperature, concentration, time of reaction, injection speed and time of injection. It is claimed that control over Ag nanostructures shape could be improved and significant information about the synthesis process can be obtained. The design of experiments was done aimed to obtain useful information about how to yield as much as possible specific structures of interest.

Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source Software

2020 ◽  
Vol 27 (05) ◽  
pp. 2040011
Author(s):  
Ruchika Malhotra ◽  
Kusum Lata
Keyword(s):  
Software Maintenance ◽  
Software Metrics ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Maintenance Cost ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Minority Class ◽  
Imbalance Problem ◽  
Ensemble Techniques

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. The imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. The recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. This paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. The ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. This study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. The results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.

scholarly journals The analysis of friction properties after laser texturizing of the silicon carbide surface layer

2018 ◽  
Vol 183 ◽  
pp. 02010
Author(s):  
Norbert Radek ◽  
Agnieszka Szczotok ◽  
Renata Dwornicka
Keyword(s):  
Silicon Carbide ◽  
Surface Layer ◽  
Statistical Analysis ◽  
Design Of Experiments ◽  
Laser Beam ◽  
High Reliability ◽  
Laser Beam Machining ◽  
Before And After ◽  
High Roughness ◽  
Modification Of The Surface

The modification of the surface properties is a desired technological procedure. One of the possible method is the electro-spark deposition (ESD). Unfortunately, ESD process produces a surface with high roughness. The laser beam machining (LBM) has been involved to lower roughness of the coating made by ESD. The elements coated by ESD have been tested to determine tribological properties and they were compared before and after LBM. To achieve high reliability of the results, the test has been conducted in accordance with design of experiments methodology and the results which were obtained have been processed by a statistical analysis. The description of such an experiment performed for a silicon carbide SiC coating, the obtained results and the conclusions are included in this paper.

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