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Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

On the Reproducibility and Replicability of Deep Learning in Software Engineering

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.

Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

Software—A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]

Improving bioinformatics software quality through incorporation of software engineering practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Inter-team communication in large-scale co-located software engineering: a case study

AbstractLarge-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Study examined AI and SE transdisciplinarity to find ways of aligning them to enable development of AI-SE transdisciplinary theory. Literature review and analysis method was used. The findings are AI and SE transdisciplinarity is tacit with islands within and between them that can be linked to accelerate their transdisciplinary orientation by codification, internally developing and externally borrowing and adapting transdisciplinary theories. Lack of theory has been identified as the major barrier toward towards maturing the two disciplines as engineering disciplines. Creating AI and SE transdisciplinary theory would contribute to maturing AI and SE engineering disciplines.  Implications of study are transdisciplinary theory can support mode 2 and 3 AI and SE innovations; provide an alternative for maturing two disciplines as engineering disciplines. Study’s originality it’s first in SE, AI or their intersections.

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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2024 2024.

Automatic Image-Based Nutritional Calculator App , Kejvi Cupa

Individual Behavioral Modeling Across Games of Strategy , Logan Fields

Semi-automated Cell Annotation Framework Using Deep Learning , Abhiram Kandiyana

Predicting Gender of Author Using Large Language Models (LLMs) , Satya Uday Sanku

Context-aware Affective Behavior Modeling and Analytics , Md Taufeeq Uddin

Exploring the Use of Enhanced SWAD Towards Building Learned Models that Generalize Better to Unseen Sources , Brandon M. Weinhofer

Theses/Dissertations from 2023 2023

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

V2V and V2I Based Safety and Platooning Algorithms for Connected and Autonomous Vehicles , Omkar Dokur

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Exploring Scalability of Multimodal User Interface Design in Virtual and Augmented Reality , Sarah M. Garcia

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Secure Reconfigurable Computing Paradigms for the Next Generation of Artificial Intelligence and Machine Learning Applications , Brooks Olney

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

An Internet of Medical Things (IoMT) Approach for Remote Assessment of Head and Neck Cancer Patients , Ruchitha Chinthala

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Developing Reinforcement Learning Algorithms for Robots to Aim and Pour Solid Objects , Haoxuan Li

Computing Group-By and Aggregate in Massively Parallel Systems , Chengcheng Mou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Exploration of Energy Efficient Computing for Data-Intensive Applications , Md Adnan Zaman

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

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Top 10 Software Engineer Research Topics for 2024

Home Blog Programming Top 10 Software Engineer Research Topics for 2024

Software engineering, in general, is a dynamic and rapidly changing field that demands a thorough understanding of concepts related to programming, computer science, and mathematics. As software systems become more complicated in the future, software developers must stay updated on industry innovations and the latest trends. Working on software engineering research topics is an important part of staying relevant in the field of software engineering. 

Software engineers can do research to learn about new technologies, approaches, and strategies for developing and maintaining complex software systems. Software engineers can conduct research on a wide range of topics. Software engineering research is also vital for increasing the functionality, security, and dependability of software systems. Going for the Top Software Engineering Certification course contributes to the advancement of the field's state of the art and assures that software engineers can continue to build high-quality, effective software systems.

What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems. 

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software. 

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

List of Software Engineer Research Topics in 2024

Here is a list of Software Engineer research topics:

• Artificial Intelligence and Software Engineering
• Natural Language Processing 
• Applications of Data Mining in Software Engineering
• Data Modeling
• Verification and Validation
• Software Project Management
• Software Quality
• Software Models

Top 10 Software Engineer Research Topics

Let's discuss the top Software Engineer Research Topics in a detailed way:

1. Artificial Intelligence and Software Engineering

a. Intersections between AI and SE

The creation of AI-powered software engineering tools is one potential research area at the intersection of artificial intelligence (AI) and software engineering. These technologies use AI techniques that include machine learning, natural language processing, and computer vision to help software engineers with a variety of tasks throughout the software development lifecycle. An AI-powered code review tool, for example, may automatically discover potential flaws or security vulnerabilities in code, saving developers a lot of time and lowering the chance of human error. Similarly, an AI-powered testing tool might build test cases and analyze test results automatically to discover areas for improvement. 

Furthermore, AI-powered project management tools may aid in the planning and scheduling of projects, resource allocation, and risk management in the project. AI can also be utilized in software maintenance duties such as automatically discovering and correcting defects or providing code refactoring solutions. However, the development of such tools presents significant technical and ethical challenges, such as the necessity of large amounts of high-quality data, the risk of bias present in AI algorithms, and the possibility of AI replacing human jobs. Continuous study in this area is therefore required to ensure that AI-powered software engineering tools are successful, fair, and responsible.

b. Knowledge-based Software Engineering

Another study area that overlaps with AI and software engineering is knowledge-based software engineering (KBSE). KBSE entails creating software systems capable of reasoning about knowledge and applying that knowledge to enhance software development processes. The development of knowledge-based systems that can help software engineers in detecting and addressing complicated problems is one example of KBSE in action. To capture domain-specific knowledge, these systems use knowledge representation techniques such as ontologies, and reasoning algorithms such as logic programming or rule-based systems to derive new knowledge from already existing data. 

KBSE can be utilized in the context of AI and software engineering to create intelligent systems capable of learning from past experiences and applying that information to improvise future software development processes. A KBSE system, for example, may be used to generate code based on previous code samples or to recommend code snippets depending on the requirements of a project. Furthermore, KBSE systems could be used to improve the precision and efficiency of software testing and debugging by identifying and prioritizing bugs using knowledge-based techniques. As a result, continued research in this area is critical to ensuring that AI-powered software engineering tools are productive, fair, and responsible.

2. Natural Language Processing

a. Multimodality

Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways. 

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

b. Efficiency

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems. 

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer. 

3. Applications of Data Mining in Software Engineering

a. Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency. 

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

b. Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data. 

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders. 

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains. 

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices. 

Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability. 

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

 To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches. 

You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student. 

Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data. 

Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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📚 A curated list of papers for Software Engineers

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Papers for software engineers.

A curated list of papers that may be of interest to Software Engineering students or professionals. See the sources and selection criteria below.

Von Neumann's First Computer Program. Knuth (1970) . Computer History; Early Programming

• The Education of a Computer. Hopper (1952) .
• Recursive Programming. Dijkstra (1960) .
• Programming Considered as a Human Activity. Dijkstra (1965) .
• Goto Statement Considered Harmful. Dijkstra (1968) .
• Program development by stepwise refinement. Wirth (1971) .
• The Humble Programmer. Dijkstra (1972) .
• Computer Programming as an Art. Knuth (1974) .
• The paradigms of programming. Floyd (1979) .
• Literate Programming. Knuth (1984) .

Computing Machinery and Intelligence. Turing (1950) . Early Artificial Intelligence

• Some Moral and Technical Consequences of Automation. Wiener (1960) .
• Steps towards Artificial Intelligence. Minsky (1960) .
• ELIZA—a computer program for the study of natural language communication between man and machine. Weizenbaum (1966) .
• A Theory of the Learnable. Valiant (1984) .

A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) . Information Theory

• A Universal Algorithm for Sequential Data Compression. Ziv, Lempel (1977) .
• Fifty Years of Shannon Theory. Verdú (1998) .

Engineering a Sort Function. Bentley, McIlroy (1993) . Data Structures; Algorithms

• On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Kruskal (1956) .
• A Note on Two Problems in Connexion with Graphs. Dijkstra (1959) .
• Quicksort. Hoare (1962) .
• Space/Time Trade-offs in Hash Coding with Allowable Errors. Bloom (1970) .
• The Ubiquitous B-Tree. Comer (1979) .
• Programming pearls: Algorithm design techniques. Bentley (1984) .
• Programming pearls: The back of the envelope. Bentley (1984) .
• Making data structures persistent. Driscoll et al (1986) .

A Design Methodology for Reliable Software Systems. Liskov (1972) . Software Design

• On the Criteria To Be Used in Decomposing Systems into Modules. Parnas (1971) .
• Information Distribution Aspects of Design Methodology. Parnas (1972) .
• Designing Software for Ease of Extension and Contraction. Parnas (1979) .
• Programming as Theory Building. Naur (1985) .
• Software Aging. Parnas (1994) .
• Towards a Theory of Conceptual Design for Software. Jackson (2015) .

Programming with Abstract Data Types. Liskov, Zilles (1974) . Abstract Data Types; Object-Oriented Programming

• The Smalltalk-76 Programming System Design and Implementation. Ingalls (1978) .
• A Theory of Type Polymorphism in Programming. Milner (1978) .
• On understanding types, data abstraction, and polymorphism. Cardelli, Wegner (1985) .
• SELF: The Power of Simplicity. Ungar, Smith (1991) .

Why Functional Programming Matters. Hughes (1990) . Functional Programming

• Recursive Functions of Symbolic Expressions and Their Computation by Machine. McCarthy (1960) .
• The Semantics of Predicate Logic as a Programming Language. Van Emden, Kowalski (1976) .
• Can Programming Be Liberated from the von Neumann Style? Backus (1978) .
• The Semantic Elegance of Applicative Languages. Turner (1981) .
• The essence of functional programming. Wadler (1992) .
• QuickCheck: A Lightweight Tool for Random Testing of Haskell Programs. Claessen, Hughes (2000) .
• Church's Thesis and Functional Programming. Turner (2006) .

An Incremental Approach to Compiler Construction. Ghuloum (2006) . Language Design; Compilers

• The Next 700 Programming Languages. Landin (1966) .
• Programming pearls: little languages. Bentley (1986) .
• The Essence of Compiling with Continuations. Flanagan et al (1993) .
• A Brief History of Just-In-Time. Aycock (2003) .
• LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. Lattner, Adve (2004) .
• A Unified Theory of Garbage Collection. Bacon, Cheng, Rajan (2004) .
• A Nanopass Framework for Compiler Education. Sarkar, Waddell, Dybvig (2005) .
• Bringing the Web up to Speed with WebAssembly. Haas (2017) .

No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) . Software Engineering; Project Management

• How do committees invent? Conway (1968) .
• Managing the Development of Large Software Systems. Royce (1970) .
• The Mythical Man Month. Brooks (1975) .
• On Building Systems That Will Fail. Corbató (1991) .
• The Cathedral and the Bazaar. Raymond (1998) .
• Out of the Tar Pit. Moseley, Marks (2006) .

Communicating sequential processes. Hoare (1978) . Concurrency

• Solution Of a Problem in Concurrent Program Control. Dijkstra (1965) .
• Monitors: An operating system structuring concept. Hoare (1974) .
• On the Duality of Operating System Structures. Lauer, Needham (1978) .
• Software Transactional Memory. Shavit, Touitou (1997) .

The UNIX Time- Sharing System. Ritchie, Thompson (1974) . Operating Systems

• An Experimental Time-Sharing System. Corbató, Merwin Daggett, Daley (1962) .
• The Structure of the "THE"-Multiprogramming System. Dijkstra (1968) .
• The nucleus of a multiprogramming system. Hansen (1970) .
• Reflections on Trusting Trust. Thompson (1984) .
• The Design and Implementation of a Log-Structured File System. Rosenblum, Ousterhout (1991) .

A Relational Model of Data for Large Shared Data Banks. Codd (1970) . Databases

• Granularity of Locks and Degrees of Consistency in a Shared Data Base. Gray et al (1975) .
• Access Path Selection in a Relational Database Management System. Selinger et al (1979) .
• The Transaction Concept: Virtues and Limitations. Gray (1981) .
• The design of POSTGRES. Stonebraker, Rowe (1986) .
• Rules of Thumb in Data Engineering. Gray, Shenay (1999) .

A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) . Networking

• Ethernet: Distributed packet switching for local computer networks. Metcalfe, Boggs (1978) .
• End-To-End Arguments in System Design. Saltzer, Reed, Clark (1984) .
• An algorithm for distributed computation of a Spanning Tree in an Extended LAN. Perlman (1985) .
• The Design Philosophy of the DARPA Internet Protocols. Clark (1988) .
• TOR: The second generation onion router. Dingledine et al (2004) .
• Why the Internet only just works. Handley (2006) .
• The Network is Reliable. Bailis, Kingsbury (2014) .

New Directions in Cryptography. Diffie, Hellman (1976) . Cryptography

• A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. Rivest, Shamir, Adleman (1978) .
• How To Share A Secret. Shamir (1979) .
• A Digital Signature Based on a Conventional Encryption Function. Merkle (1987) .
• The Salsa20 family of stream ciphers. Bernstein (2007) .

Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) . Distributed Systems

• Self-stabilizing systems in spite of distributed control. Dijkstra (1974) .
• The Byzantine Generals Problem. Lamport, Shostak, Pease (1982) .
• Impossibility of Distributed Consensus With One Faulty Process. Fisher, Lynch, Patterson (1985) .
• Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial. Schneider (1990) .
• Practical Byzantine Fault Tolerance. Castro, Liskov (1999) .
• Paxos made simple. Lamport (2001) .
• Paxos made live - An Engineering Perspective. Chandra, Griesemer, Redstone (2007) .
• In Search of an Understandable Consensus Algorithm. Ongaro, Ousterhout (2014) .

Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) . Human-Computer Interaction; User Interfaces

• As We May Think. Bush (1945) .
• Man-Computer symbiosis. Licklider (1958) .
• Some Thoughts About the Social Implications of Accessible Computing. David, Fano (1965) .
• Tutorials for the First-Time Computer User. Al-Awar, Chapanis, Ford (1981) .
• The star user interface: an overview. Smith, Irby, Kimball (1982) .
• Design Principles for Human-Computer Interfaces. Norman (1983) .
• Human-Computer Interaction: Psychology as a Science of Design. Carroll (1997) .

The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) . Information Retrieval; World-Wide Web

• A Statistical Interpretation of Term Specificity in Retrieval. Spärck Jones (1972) .
• World-Wide Web: Information Universe. Berners-Lee et al (1992) .
• The PageRank Citation Ranking: Bringing Order to the Web. Page, Brin, Motwani (1998) .

Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) . Internet Scale Data Systems

• The Google File System. Ghemawat, Gobioff, Leung (2003) .
• MapReduce: Simplified Data Processing on Large Clusters. Dean, Ghemawat (2004) .
• Bigtable: A Distributed Storage System for Structured Data. Chang et al (2006) .
• ZooKeeper: wait-free coordination for internet scale systems. Hunt et al (2010) .
• The Hadoop Distributed File System. Shvachko et al (2010) .
• Kafka: a Distributed Messaging System for Log Processing. Kreps, Narkhede, Rao (2011) .
• CAP Twelve Years Later: How the "Rules" Have Changed. Brewer (2012) .
• Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. Verbitski et al (2017) .

On Designing and Deploying Internet Scale Services. Hamilton (2007) . Operations; Reliability; Fault-tolerance

• Ironies of Automation. Bainbridge (1983) .
• Why do computers stop and what can be done about it? Gray (1985) .
• Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies. Patterson et al (2002) .
• Crash-Only Software. Candea, Fox (2003) .
• Building on Quicksand. Helland, Campbell (2009) .

Thinking Methodically about Performance. Gregg (2012) . Performance

• Performance Anti-Patterns. Smaalders (2006) .
• Thinking Clearly about Performance. Millsap (2010) .

Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) . Decentralized Distributed Systems; Peer-to-peer systems

• Operational transformation in real-time group editors: issues, algorithms, and achievements. Sun, Ellis (1998) .
• Kademlia: A Peer-to-Peer Information System Based on the XOR Metric. Maymounkov, Mazières (2002) .
• Incentives Build Robustness in BitTorrent. Cohen (2003) .
• Conflict-free Replicated Data Types. Shapiro et al (2011) .
• IPFS - Content Addressed, Versioned, P2P File System. Benet (2014) .
• Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Buterin (2014) .
• Local-First Software: You Own Your Data, in spite of the Cloud. Kleppmann et al (2019) .

A Few Useful Things to Know About Machine Learning. Domingos (2012) . Machine Learning

• Statistical Modeling: The Two Cultures. Breiman (2001) .
• The Unreasonable Effectiveness of Data. Halevy, Norvig, Pereira (2009) .
• ImageNet Classification with Deep Convolutional Neural Networks. Krizhevsky, Sutskever, Hinton (2012) .
• Playing Atari with Deep Reinforcement Learning. Mnih et al (2013) .
• Generative Adversarial Nets. Goodfellow et al (2014) .
• Deep Learning. LeCun, Bengio, Hinton (2015) .
• Attention Is All You Need. Vaswani et al (2017) .
• Von Neumann's First Computer Program. Knuth (1970) .
• Computing Machinery and Intelligence. Turing (1950) .
• A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) .
• Engineering a Sort Function. Bentley, McIlroy (1993) .
• A Design Methodology for Reliable Software Systems. Liskov (1972) .
• Programming with Abstract Data Types. Liskov, Zilles (1974) .
• Why Functional Programming Matters. Hughes (1990) .
• An Incremental Approach to Compiler Construction. Ghuloum (2006) .
• No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) .
• Communicating sequential processes. Hoare (1978) .
• The UNIX Time- Sharing System. Ritchie, Thompson (1974) .
• A Relational Model of Data for Large Shared Data Banks. Codd (1970) .
• A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) .
• New Directions in Cryptography. Diffie, Hellman (1976) .
• Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) .
• Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) .
• The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) .
• Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) .
• On Designing and Deploying Internet Scale Services. Hamilton (2007) .
• Thinking Methodically about Performance. Gregg (2012) .
• Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) .
• A Few Useful Things to Know About Machine Learning. Domingos (2012) .

This list was inspired by (and draws from) several books and paper collections:

• Papers We Love
• Ideas That Created the Future
• The Innovators
• The morning paper
• Distributed systems for fun and profit
• Readings in Database Systems (the Red Book)
• Fermat's Library
• Classics in Human-Computer Interaction
• Awesome Compilers
• Distributed Consensus Reading List
• The Decade of Deep Learning

A few interesting resources about reading papers from Papers We Love and elsewhere:

• Should I read papers?
• How to Read an Academic Article
• How to Read a Paper. Keshav (2007) .
• Efficient Reading of Papers in Science and Technology. Hanson (1999) .
• On ICSE’s “Most Influential Papers”. Parnas (1995) .

Selection criteria

• The idea is not to include every interesting paper that I come across but rather to keep a representative list that's possible to read from start to finish with a similar level of effort as reading a technical book from cover to cover.
• I tried to include one paper per each major topic and author. Since in the process I found a lot of noteworthy alternatives, related or follow-up papers and I wanted to keep track of those as well, I included them as sublist items.
• The papers shouldn't be too long. For the same reasons as the previous item, I try to avoid papers longer than 20 or 30 pages.
• They should be self-contained and readable enough to be approachable by the casual technical reader.
• They should be freely available online.
• Examples of this are classic works by Von Neumann, Turing and Shannon.
• That being said, where possible I preferred the original paper on each subject over modern updates or survey papers.
• Similarly, I tended to skip more theoretical papers, those focusing on mathematical foundations for Computer Science, electronic aspects of hardware, etc.
• I sorted the list by a mix of relatedness of topics and a vague chronological relevance, such that it makes sense to read it in the suggested order. For example, historical and seminal topics go first, contemporary internet-era developments last, networking precedes distributed systems, etc.

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Over recent years, software developers have been evaluating the benefits of both Service-Oriented Architecture (SOA) and software fault tolerance techniques based on design diversity. This is achieved by creat...

Method-level code clone detection through LWH (Light Weight Hybrid) approach

Many researchers have investigated different techniques to automatically detect duplicate code in programs exceeding thousand lines of code. These techniques have limitations in finding either the structural o...

The problem of conceptualization in god class detection: agreement, strategies and decision drivers

The concept of code smells is widespread in Software Engineering. Despite the empirical studies addressing the topic, the set of context-dependent issues that impacts the human perception of what is a code sme...

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