arXiv subjects

Asif Imran

Publications and source records attributed to Asif Imran.

At least 19 recordsLinked to original sources

arXiv2025

Energy Consumption of Dataframe Libraries for End-to-End Deep Learning Pipelines:A Comparative Analysis

This paper presents a detailed comparative analysis of the performance of three major Python data manipulation libraries - Pandas, Polars, and Dask - specifically when embedded within complete deep learning (DL) training and inference pipelines. The research bridges a gap in existing literature by studying how these libraries interact with substantial GPU workloads during critical phases like data loading, preprocessing, and batch feeding. The authors measured key performance indicators including runtime, memory usage, disk usage, and energy consumption (both CPU and GPU) across various machine learning models and datasets.

cs.SE↗

arXiv2025

Evaluating the Energy-Efficiency of the Code Generated by LLMs

As the quality of code generated by Large Language Models (LLMs) improves, their adoption in the software industry for automated code generation continues to grow. Researchers primarily focus on enhancing the functional correctness of the generated code while commonly overlooking its energy efficiency and environmental impact. This paper investigates the energy efficiency of the code generated by 20 popular LLMs for 878 programming problems of varying difficulty levels and diverse algorithmic categories selected from the LeetCode platform by comparing them against canonical human-written solutions. Although LLMs can produce functionally correct results in most cases, our findings show that the performance and energy efficiency of LLM-produced solutions are often far below those of human-written solutions. Among the studied LLMs, DeepSeek-v3 and GPT-4o generate the most energy-efficient code, whereas Grok-2 and Gemini-1.5-Pro are among the least energy-efficient models. On average, human-generated canonical solutions are approximately 1.17 times more energy efficient than DeepSeek-v3, 1.21 times more energy efficient than GPT-4o, and over 2 times more energy efficient than Grok-2 and Gemini-1.5-Pro. For specific algorithmic groups such as dynamic programming, backtracking, and bit manipulation, LLM-generated code can consume up to 450 times more energy than human-generated canonical solutions.

cs.SE↗

arXiv2023

URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud

The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.

cs.SE↗

arXiv2023

Investigate how developers and managers view security design in software

Software security requirements have been traditionally considered as a non-functional attribute of the software. However, as more software started to provide services online, existing mechanisms of using firewalls and other hardware to secure software have lost their applicability. At the same time, under the current world circumstances, the increase of cyber-attacks on software is ever increasing. As a result, it is important to consider the security requirements of software during its design. To design security in the software, it is important to obtain the views of the developers and managers of the software. Also, it is important to evaluate if their viewpoints match or differ regarding the security. Conducting this communication through a specific model will enable the developers and managers to eliminate any doubts on security design and adopt an effective strategy to build security into the software. In this paper, we analyzed the viewpoints of developers and managers regarding their views on security design. We interviewed a team of 7 developers and 2 managers, who worked in two teams to build a real-life software product that was recently compromised by a cyber-attack. We obtained their views on the reasons for the successful attack by the malware and took their recommendations on the important aspects to consider regarding security. Based on their feedback, we coded their open-ended responses into 4 codes, which we recommended using for other real-life software as well.

cs.SE↗

arXiv2023

Qualitative analysis of the relationship between design smells and software engineering challenges

Software design debt aims to elucidate the rectification attempts of the present design flaws and studies the influence of those to the cost and time of the software. Design smells are a key cause of incurring design debt. Although the impact of design smells on design debt have been predominantly considered in current literature, how design smells are caused due to not following software engineering best practices require more exploration. This research provides a tool which is used for design smell detection in Java software by analyzing large volume of source codes. More specifically, 409,539 Lines of Code (LoC) and 17,760 class files of open source Java software are analyzed here. Obtained results show desirable precision values ranging from 81.01\% to 93.43\%. Based on the output of the tool, a study is conducted to relate the cause of the detected design smells to two software engineering challenges namely "irregular team meetings" and "scope creep". As a result, the gained information will provide insight to the software engineers to take necessary steps of design remediation actions.

cs.SE↗

arXiv2023

CloudScent: a model for code smell analysis in open-source cloud

The low cost and rapid provisioning capabilities have made open-source cloud a desirable platform to launch industrial applications. However, as open-source cloud moves towards maturity, it still suffers from quality issues like code smells. Although, a great emphasis has been provided on the economic benefits of deploying open-source cloud, low importance has been provided to improve the quality of the source code of the cloud itself to ensure its maintainability in the industrial scenario. Code refactoring has been associated with improving the maintenance and understanding of software code by removing code smells. However, analyzing what smells are more prevalent in cloud environment and designing a tool to define and detect those smells require further attention. In this paper, we propose a model called CloudScent which is an open source mechanism to detect smells in open-source cloud. We test our experiments in a real-life cloud environment using OpenStack. Results show that CloudScent is capable of accurately detecting 8 code smells in cloud. This will permit cloud service providers with advanced knowledge about the smells prevalent in open-source cloud platform, thus allowing for timely code refactoring and improving code quality of the cloud platforms.

cs.SE↗

arXiv2023

Predicting the Impact of Batch Refactoring Code Smells on Application Resource Consumption

Automated batch refactoring has become a de-facto mechanism to restructure software that may have significant design flaws negatively impacting the code quality and maintainability. Although automated batch refactoring techniques are known to significantly improve overall software quality and maintainability, their impact on resource utilization is not well studied. This paper aims to bridge the gap between batch refactoring code smells and consumption of resources. It determines the relationship between software code smell batch refactoring, and resource consumption. Next, it aims to design algorithms to predict the impact of code smell refactoring on resource consumption. This paper investigates 16 code smell types and their joint effect on resource utilization for 31 open source applications. It provides a detailed empirical analysis of the change in application CPU and memory utilization after refactoring specific code smells in isolation and in batches. This analysis is then used to train regression algorithms to predict the impact of batch refactoring on CPU and memory utilization before making any refactoring decisions. Experimental results also show that our ANN-based regression model provides highly accurate predictions for the impact of batch refactoring on resource consumption. It allows the software developers to intelligently decide which code smells they should refactor jointly to achieve high code quality and maintainability without increasing the application resource utilization. This paper responds to the important and urgent need of software engineers across a broad range of software applications, who are looking to refactor code smells and at the same time improve resource consumption. Finally, it brings forward the concept of resource aware code smell refactoring to the most crucial software applications.

cs.SE↗

arXiv2020

The Impact of Auto-Refactoring Code Smells on the Resource Utilization of Cloud Software

Cloud-based software-as-a-service (SaaS) have gained popularity due to their low cost and elasticity. However, like other software, SaaS applications suffer from code smells, which can drastically affect functionality and resource usage. Code smell is any design in the source code that indicates a deeper problem. The software community deploys automated refactoring to eliminate smells which can improve performance and also decrease the usage of critical resources. However, studies that analyze the impact of automatic refactoring smells in SaaS on resources such as CPU and memory have been conducted to a limited extent. Here, we aim to fill that gap and study the impact on resource usage of SaaS applications due to automatic refactoring of seven classic code smells: god class, feature envy, type checking, cyclic dependency, shotgun surgery, god method, and spaghetti code. We specified six real-life SaaS applications from Github called Zimbra, OneDataShare, GraphHopper, Hadoop, JENA, and JAMES which ran on Openstack cloud. Results show that refactoring smells by tools like JDeodrant and JSparrow have widely varying impacts on the CPU and memory consumption of the tested applications based on the type of smell refactored. We present the resource utilization impact of each smell and also discuss the potential reasons leading to that effect.

cs.SE↗

arXiv2020

Effects of Internship on Fresh Graduates: A case study on IIT, DU students

The aim of any curriculum is to produce industry ready students. The effectiveness of curricular activities, thus, can be measured by the performances of fresh graduates at their job sectors. To evaluate the Software Engineering (SE) syllabus, Institute of Information Technology (IIT), University of Dhaka, has taken an initiative, under the project IQAC, HEQEP, where a survey based study has been performed. The uniqueness of this SE syllabus is having a six month long internship semester inside the curriculum. Considering all the other courses and activities as traditional, the outcome of the study can fairly be considered as the effect of the Internship program. The result shows that the students having internship experiences, performed above the level of expectation from the industries.

cs.CY↗

arXiv2019

Design Smell Analysis for Developing and Established Open Source Java Software

Software design smells are design attributes which violate the fundamental design principles. Design smells are a key cause of design debt. Although the activities of design smell identification and measurement are predominantly considered in current literature, those which identify and communicate which design smells occur more frequently in newly developing software and which ones are more dominant in established software have been studied to a limited extent. This research describes a mechanism for identifying the design smells that are more prevalent in developing and established software respectively. A tool is provided which is used for design smell detection by analyzing large volumes of source code. More specifically, 164,609 Lines of Code (LoC) and 5,712 class files of six developing and 244,930 LoC and 12,048 class files of five established open-source Java software are analyzed. Obtained results show that out of the 4,020 occurrences of smells that were made for nine preselected types of design smells, 1,643 design smells were detected for developing software, which mainly consisted of four specific types of smells. For established software, 2,397 design smells were observed which predominantly consisted of four other types of smells. The remaining design smell was equally prevalent in both developing and established software. Desirable precision values ranging from 72.9% to 84.1% were obtained for the tool.

cs.SE↗

arXiv2019

Software Sustainability: A Systematic Literature Review and Comprehensive Analysis

Software Engineering is a constantly evolving subject area that faces new challenges every day as it tries to automate newer business processes. One of the key challenges to the success of a software solution is attaining sustainability. The inability of numerous software to sustain for the desired time-length is caused by limited consideration given towards sustainability during the stages of software development. This review aims to present a detailed and inclusive study covering both the technical and non-technical challenges and approaches of software sustainability. A systematic and comprehensive literature review was conducted based on 107 relevant studies that were selected using the Evidence-Based Software Engineering (EBSE) technique. The study showed that sustainability can be achieved by conducting specific activities at the technical and non-technical levels. The technical level consists of software design, coding, and user experience attributes. The non-technical level consists of documentation, sustainability manifestos, training of software engineers, funding software projects, and leadership skills of project managers to achieve sustainability. This paper groups the existing research efforts based on the above aspects. Next, how those aspects affect open and closed source software is tabulated. Based on the findings of this review, it is seen that both technical and non-technical sustainability aspects are equally important, taking one into contention and ignoring the other will threaten the sustenance of software products.

cs.SE↗

arXiv2018

GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5x for the achieved end-to-end performance.

cs.NI↗

arXiv2017

OneDataShare: A Vision for Cloud-hosted Data Transfer Scheduling and Optimization as a Service

Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for data-intensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for business intelligence. The paper elaborates on the desirable features of OneDataShare as a cloud-hosted data transfer scheduling and optimization service, and how it is aligned with the vision of harnessing the power of the cloud and distributed computing. Experimental evaluation and comparison with existing real-life file transfer services show that the transfer throughout achieved by OneDataShare is 6.5 times greater.

cs.DC↗

arXiv2014

GPS Timing and Control System of the HAWC Detector

The design and performance of the GPS Timing and Control (GTC) System of the High Altitude Water Cerenkov (HAWC) gamma ray observatory is described. The GTC system provides a GPS synchronized absolute timestamp, with an accuracy better than 1$μ$s, for each recorded event in HAWC. In order to avoid any slack between the recorded data and the timestamp, timestamps are injected to the main data acquisition (DAQ) system after the Front-end Electronic Boards (FEBs). When HAWC is completed, the HAWC main DAQ will use 10 time to digital converters (TDCs). In order to keep all the TDCs in sync, the GTC system provides a synchronized clock signal, coordinated trigger signal, and control signals to all TDCs.

astro-ph.IM↗

arXiv2014

Watchword-Oriented and Time-Stamped Algorithms for Tamper-Proof Cloud Provenance Cognition

Provenance is derivative journal information about the origin and activities of system data and processes. For a highly dynamic system like the cloud, provenance can be accurately detected and securely used in cloud digital forensic investigation activities. This paper proposes watchword oriented provenance cognition algorithm for the cloud environment. Additionally time-stamp based buffer verifying algorithm is proposed for securing the access to the detected cloud provenance. Performance analysis of the novel algorithms proposed here yields a desirable detection rate of 89.33% and miss rate of 8.66%. The securing algorithm successfully rejects 64% of malicious requests, yielding a cumulative frequency of 21.43 for MR.

cs.DC↗

arXiv2014

Active-Threaded Algorithms for Provenance Cognition in the Cloud preserving Low Overhead and Fault Tolerance

Provenance is the derivation history of information about the origin of data and processes. For a highly dynamic system such as the cloud, provenance must be effectively detected to be used as proves to ensure accountability during digital forensic investigations. This paper proposes active-threaded provenance cognition algorithms that ensure effective and high speed detection of provenance information in the activity layer of the cloud. The algorithms also support encapsulation of the provenance information on specific targets. Performance evaluation of the proposed algorithms reveal mean delay of 8.198 seconds that is below the pre-defined benchmark of 10 seconds. Standard deviation and cumulative frequencies for delays are found to be 1.434 and 45.1% respectively.

cs.DC↗

arXiv2014

TFPaaS : Test-first Performance as a Service to Cloud for Software Testing Environment

Performance Testing is critical for applications like web services and e-commerce platforms to ensure enhanced end user experience. In such cases, starting to test the system's performance early should significantly reduce the overall development cost. Test-first Performance (TFP) is one such paradigm that allows performance testing right from the early stage of development. Given such potential benefit, this paper proposes the design of a testing framework IVRIDIO which introduces TFP as a Service (TFPaaS). IVRIDIO incorporates the Plugin for TFP in the Cloud (PTFPC) aiming to provide instant feedbacks - a prime requirement of TFP to immediately fix critical performance issues. Furthermore, the Convention over Configuration (CoC) design paradigm has been applied by introducing a configurable project template to maintain TFP test cases. The prototyping details of the framework are given and the variation of response time to the inclusion of PTFPC has also been discussed. The Summated Usability Metric (SUM) score has been provided so that it can later be used for comparing the PTFPC plugin's usability.

cs.SE↗

arXiv2010

Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images

Object detection has been a focus of research in human-computer interaction. Skin area detection has been a key to different recognitions like face recognition, human motion detection, pornographic and nude image prediction, etc. Most of the research done in the fields of skin detection has been trained and tested on human images of African, Mongolian and Anglo-Saxon ethnic origins. Although there are several intensity invariant approaches to skin detection, the skin color of Indian sub-continentals have not been focused separately. The approach of this research is to make a comparative study between three image segmentation approaches using Indian sub-continental human images, to optimize the detection criteria, and to find some efficient parameters to detect the skin area from these images. The experiments observed that HSV color model based approach to Indian sub-continental skin detection is more suitable with considerable success rate of 91.1% true positives and 88.1% true negatives.

cs.CV↗