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Journal articles on the topic 'Software Engineering Productivity'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Hernández-López, Adrián, Ricardo Colomo-Palacios, Ángel García-Crespo, and Fernando Cabezas-Isla. "Software Engineering Productivity." International Journal of Information Technology Project Management 2, no. 1 (2011): 37–47. http://dx.doi.org/10.4018/jitpm.2011010103.

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Software engineering productivity has been widely studied, but there are many issues that remain unsolved. Interesting works related to new metrics and more replications of past productivity analysis have emerged, however, in order to fulfill these unsolved issues, a consensus about influencing factors and well recognized and useful sets of inputs and outputs for using in measurements must be reached. In this regard, a clear state of the art may shed light on further research in software engineering productivity, which remains a promising research area. In this paper, general concepts of softw

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2

Beam, Walter R., James D. Palmer, and Andrew P. Sage. "Systems Engineering for Software Productivity." IEEE Transactions on Systems, Man, and Cybernetics 17, no. 2 (1987): 163–86. http://dx.doi.org/10.1109/tsmc.1987.4309028.

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3

Hernández-López, Adrián, Ricardo Colomo-Palacios, Pedro Soto-Acosta, and Cristina Casado Lumberas. "Productivity Measurement in Software Engineering." International Journal of Information Technologies and Systems Approach 8, no. 1 (2015): 46–68. http://dx.doi.org/10.4018/ijitsa.2015010103.

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Productivity measurement is constructed by the measure of tree categories of elements: inputs, outputs and factors. This concept, which started being used in the manufacturing industry, has been also a research topic within Software Engineering (SE). In this area, the most used inputs are time and effort and the most used outputs are source code and functionality. Despite of their known limitations, many of the most used productivity measures are still being used due to the information that they provide for management goals. In order to enable the construction of new productivity measures for

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4

Yu, Weider D., D. Paul Smith, and Steel T. Huang. "Software Productivity Measurements." AT&T Technical Journal 69, no. 3 (1990): 110–20. http://dx.doi.org/10.1002/j.1538-7305.1990.tb00339.x.

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5

HERNÁNDEZ-LÓPEZ, ADRIÁN, RICARDO COLOMO-PALACIOS, and ÁNGEL GARCÍA-CRESPO. "SOFTWARE ENGINEERING JOB PRODUCTIVITY — A SYSTEMATIC REVIEW." International Journal of Software Engineering and Knowledge Engineering 23, no. 03 (2013): 387–406. http://dx.doi.org/10.1142/s0218194013500125.

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Productivity is a key element in organizational management. Although it can be measured at different levels (country, sector, organization …) this research focuses on productivity at the job level. The aim of this paper is to obtain an overview of the state of the art in productivity measurement in software engineering, including the inputs and outputs of the production process used for this measurement at the job level in the workplace. To do so, a systematic literature review protocol was adapted from literature review protocol standards, and subsequently carried out. The objective is to ass

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Norman, Ronald J., and Jay F. Nunamaker. "CASE productivity perceptions of software engineering professionals." Communications of the ACM 32, no. 9 (1989): 1102–8. http://dx.doi.org/10.1145/66451.66458.

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7

Biswas, Saumen. "Predictive Metrics: Transforming Engineering Productivity and Software Quality." International Journal of Computer Trends and Technology 73, no. 1 (2025): 51–56. https://doi.org/10.14445/22312803/ijctt-v73i1p106.

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Aramo-Immonen, Heli, Hannu Jaakkola, and Harri Keto. "Multicultural Software Development." International Journal of Information Technology Project Management 2, no. 1 (2011): 19–36. http://dx.doi.org/10.4018/jitpm.2011010102.

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Productivity management is a challenge for software engineering companies and, in this regard, there is a current trend toward globalization. Via acquisitions and mergers, business has become international and employs different national cultures. Therefore, the focus of this article is on the understanding of cultural differences affecting productivity in globalized software production. The relation between productivity and non coding activities in software development projects has not been proven. Software development is expert work, typically made in closely collaborating local teams and glo

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Thilmany, Jean. "Pushing Productivity." Mechanical Engineering 123, no. 12 (2001): 48–50. http://dx.doi.org/10.1115/1.2001-dec-3.

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This article reviews computer-aided engineering software that is used to boost productivity ranges from computer-aided design (CAD) systems to product data management and visualization systems. MacDon Industries used a Solid Edge product to merge CAD and product data management (PDM) systems to give engineers easy access to already created designs. MacDon tracks its tens of thousands of part designs by use of Solid Edge so engineers do not have to spend considerable time searching for the designs. Bayside Automation of Canonsburg, Pennsylvania, which makes automated assembly systems on which f

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10

Kontsevoi, Boris. "Predictive Software Engineering: Empowering Business Success with Tailored Software Solutions." International Journal of Software Engineering & Applications 15, no. 3 (2024): 01–12. http://dx.doi.org/10.5121/ijsea.2024.15301.

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This paper explores the seven core principles of the Predictive Software Engineering (PSE) framework. These principles are designed to empower custom software development companies to deliver transparent and reliable solutions, all while adhering to predetermined budgets. The paper delves into each of the seven principles: Meaningful Customer Care, Transparent End-to-End Control, Proven Productivity, Efficient Distributed Teams, Disciplined Agile Delivery Process, Measurable Quality Management and Technical Debt Reduction, and Sound Human Development.

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Sochat, Vanessa. "The 10 best practices for remote software engineering." Communications of the ACM 64, no. 5 (2021): 32–36. http://dx.doi.org/10.1145/3459613.

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12

Faulk, Stuart, Eugene Loh, Michael L. Van De Vanter, Susan Squires, and Lawrence G. Votta. "Scientific Computing's Productivity Gridlock: How Software Engineering Can Help." Computing in Science & Engineering 11, no. 6 (2009): 30–39. http://dx.doi.org/10.1109/mcse.2009.205.

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13

Bok, Hai Suan, and K. S. Raman. "Software Engineering Productivity Measurement using Function Points: A Case Study." Journal of Information Technology 15, no. 1 (2000): 79–90. http://dx.doi.org/10.1177/026839620001500108.

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This paper reports on the findings of an empirically based case study of the use of function points analysis (FPA) by the information systems division of a large financial services company. The software engineering productivity figures measured by FPA in this company varied widely across the departments of the division and projects. Investigation of the reasons for the variations showed that, in addition to factors such as the technology platform and application characteristics, organizational and human factors affect the accuracy and reliability of productivity figures. Elucidating the lesson

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Mohammed, Alnajjar, and Samy S. Abu Naser Prof. "EVALUATING SOFTWARE ENGINEERING PRACTICES IN PALESTINE." International Journal of Soft Computing, Mathematics and Control (IJSCMC) 4, no. 1 (2018): 35–47. https://doi.org/10.14810/ijscmc.2015.4104.

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Software Engineering, the art and science of building improved software, is a core requirement of the majority computer science curriculum. Our ongoing research program focuses on three aspects of the global development of software in Palestine: (1) collection of quantitative data regarding current practice and performance in a variety of organizations; (2) determination of the adoption of competing models of software development across the organizations, and (3) analysis of the impact of different development practices on performance. This paper show the results of a survey of software develo

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15

Srinivasan, Vaishnavi, Manimegalai Rajkumar, Srivatsan Santhanam, and Arjit Garg. "PlatFab: A Platform Engineering Approach to Improve Developer Productivity." Journal of Information Systems Engineering and Business Intelligence 11, no. 1 (2025): 79–90. https://doi.org/10.20473/jisebi.11.1.79-90.

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Background: Software developers are key players in IT/ITES business in order to drive software development by writing high-quality code quickly. Based on user needs, they must adapt evolving technologies and tools to produce efficient and successful software using Software Development Life Cycle (SDLC) principles. Platform Engineering comprises a set of activities to design, develop and maintain software code, making it a foundation for building software applications. Objective: This work focuses on reducing the time and effort needed to execute the above tasks that boosts software developer p

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16

Boris, Kontsevoi, and Kizyan Sergey. "Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions." Journal of Economics, Finance And Management Studies 5, no. 01 (2022): 73–77. https://doi.org/10.47191/jefms/v5-i1-09.

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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.

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17

Shashidhara, Narendra Subbanarasimhaiah. "AI in Software Engineering – How Intelligent Systems Are Changing the Software Development Process." European Journal of Computer Science and Information Technology 13, no. 29 (2025): 28–39. https://doi.org/10.37745/ejcsit.2013/vol13n292839.

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Artificial intelligence is fundamentally transforming software engineering practices across all phases of development, evolving from basic assistance tools to active collaborators in the creation process. This transformation represents a paradigm shift in how software is conceptualized, developed, and maintained, with substantial impacts on productivity, quality, and professional roles. The integration of AI capabilities extends throughout the entire software development lifecycle, from requirements analysis and architectural design to implementation, testing, and operations. Modern AI coding

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18

Inman, Sarah, Sarah D’Angelo, and Bogdan Vasilescu. "Developer Productivity for Humans, Part 8: Creativity in Software Engineering." IEEE Software 41, no. 2 (2024): 11–16. http://dx.doi.org/10.1109/ms.2023.3340831.

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19

Bok, Hai Suan, and K. s. Raman. "Software engineering productivity measurement using function points: a case study." Journal of Information Technology 15, no. 1 (2000): 79–90. http://dx.doi.org/10.1080/026839600344429.

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20

Lawlor, Brendan, and Paul Walsh. "Engineering bioinformatics: building reliability, performance and productivity into bioinformatics software." Bioengineered 6, no. 4 (2015): 193–203. http://dx.doi.org/10.1080/21655979.2015.1050162.

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21

Paine, R. M. "Eclipse: a software engineering tool." ITNOW 27, no. 2 (1985): 8–15. https://doi.org/10.1093/combul/27.2.8.

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Abstract In the March 1985 issue of Computer Bulletin, Mr D. Simpson described the Alvey approach to an IPSE, where the ‘P’ stood for Project. Eclipse, the Electricity Council Integrated Programming Support Environment is a suite of programs developed to provide significant improvements in analyst/programmer productivity. In terms of the diagrammatic view of the Alvey IPSE in Mr Simpson’s article, it fits into the segment ‘Other IPSE tools’, but also provides an interface with design methodologies. It has been written entirely in Delta, a machine independent Cobol generator, to aid portability

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22

Yilmaz, Murat, and Rory V. O'Connor. "A software process engineering approach to improving software team productivity using socioeconomic mechanism design." ACM SIGSOFT Software Engineering Notes 36, no. 5 (2011): 1–5. http://dx.doi.org/10.1145/2020976.2020998.

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23

Fujimura, Naomi. "Software productivity in built-in microprocessors." Microprocessing and Microprogramming 28, no. 1-5 (1990): 169–72. http://dx.doi.org/10.1016/0165-6074(90)90168-9.

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24

Factor, Robert M., and William B. Smith. "A Discipline for Improving Software Productivity." AT&T Technical Journal 67, no. 4 (1988): 2–9. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00633.x.

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25

Mesaros, D., and V. Spoiala. "Software Quality Management Framework Using HACCP in Engineering." Scientific Bulletin of Electrical Engineering Faculty 25, no. 1 (2025): 41–44. https://doi.org/10.2478/sbeef-2025-0007.

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Abstract The paper aims to demonstrate the application of HACCP to software quality management framework in steel engineering. This paper begins with a quick discussion of the theoretical foundations of quality management including quality assurance, then moves on to a brief introduction to HACCP that emphasizes its possible applications in a variety of industries. The goals of interdisciplinarity include profit maximization, enhanced productivity, and customer pleasure.

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26

Simmons, Dick B. "Communications: a software group productivity dominator." Software Engineering Journal 6, no. 6 (1991): 454. http://dx.doi.org/10.1049/sej.1991.0044.

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27

Mullen, Julie, Nadya Bliss, Robert Bond, Jeremy Kepner, Hahn Kim, and Albert Reuther. "High-Productivity Software Development with pMatlab." Computing in Science & Engineering 11, no. 1 (2009): 75–79. http://dx.doi.org/10.1109/mcse.2009.9.

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28

Duarte, Carlos Henrique C. "Software Productivity in Practice: A Systematic Mapping Study." Software 1, no. 2 (2022): 164–214. http://dx.doi.org/10.3390/software1020008.

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Practitioners perceive software productivity as one of the most important subjects of software engineering (SE) because it connects technical to social and economic aspects. Nonetheless, software processes are complex and productivity means different things to different people. In order to realize the full contribution of software productivity research to the industrial practice of SE, the analysis and synthesis of existing practitioner viewpoints and concerns are required. A systematic mapping study is developed here to investigate the existence of diverse empirical perceptions of productivit

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29

Priyadharasini M, Sriram S N, Sudhar Aathith T, and Vigneshwaran N. "Steve Jobs: Pioneering AI in Software Engineering." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 04 (2024): 823–29. http://dx.doi.org/10.47392/irjaeh.2024.0116.

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"STEVE JOBS: Pioneering AI in Software Engineering" presents a revolutionary approach to software development, integrating large language models (LLMs) into traditional methodologies. This paradigm, inspired by the visionary leadership of Steve Jobs, leverages LLMs to streamline the software development lifecycle (SDLC), incorporating both the waterfall model and agile methodology. The implementation involves the orchestration of software agents, representing various roles in the development process, fostering collaborative dialogue through natural language communication. This innovative frame

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30

Kwon, Oh Cheon, Cornelia Boldyreff, and Malcolm Munro. "Software Configuration Management for a Reusable Software Library within a Software Maintenance Environment." International Journal of Software Engineering and Knowledge Engineering 08, no. 04 (1998): 483–515. http://dx.doi.org/10.1142/s0218194098000273.

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Software Configuration Management and software reuse are now generally considered to be important technologies in software engineering. Both have been proposed for making a significant improvement in productivity and quality. However, these two technologies have been investigated separately. In order to make Software Configuration Management and reuse more effective, both approaches require to be introduced into a development or maintenance environment together. The authors have developed a process model for Maintenance with Reuse (MwR) to support Software Configuration Management for a reuse

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31

Cerveny, Robert P., and Daniel A. Joseph. "A study of the effects of three commonly used software engineering strategies on software enhancement productivity." Information & Management 14, no. 5 (1988): 243–51. http://dx.doi.org/10.1016/0378-7206(88)90012-2.

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32

Duchesne, D. P. J., and J. L. Humar. "Engineering software – a structural consultant's perspective." Canadian Journal of Civil Engineering 18, no. 2 (1991): 303–11. http://dx.doi.org/10.1139/l91-035.

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The advantages that automatic computing offers in structural analysis, design, and drafting are now well known. Computers offer great speed and accuracy in the process of analysis and design, relieving the engineer of the drudgery of number crunching, permitting more time for innovation and creativity, and affording the opportunity of trying out several design alternatives. However, to realize the productivity gains and the improvements in design quality that computers offer, the engineer must be aware of the many pitfalls and problems associated with computerization: the financial commitment,

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33

Sun, Yu Qiang, Shi Yan Xie, Yu Wan Gu, and Hai Feng Shi. "The Design of Software Parallel Engineering Model Based on Petri Net." Advanced Materials Research 186 (January 2011): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.186.317.

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In the paper, Using Petri net to a component-based reuse-driven software model for parallel engineering is proposed. The model applies Petri net to analyze parallelism in the entire software process. The model excavates fully Parallelism during software life cycle, supports various parallel of different particle size coarse and fine. The model can greatly enhance the software development, software productivity and shorten the development cycle.

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Banker, Rajiv D., and Robert J. Kauffman. "Reuse and Productivity in Integrated Computer-Aided Software Engineering: An Empirical Study." MIS Quarterly 15, no. 3 (1991): 375. http://dx.doi.org/10.2307/249649.

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35

Linjawi, Hussam H., and Brian H. Kleiner. "Software Packages: Tools for Management Productivity." Industrial Management & Data Systems 89, no. 6 (1989): 27–31. http://dx.doi.org/10.1108/eum0000000001411.

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36

Pfahl, Dietmar. "An Interview with Nachiappan Nagappan - ACM Fellow 2020." ACM SIGSOFT Software Engineering Notes 46, no. 4 (2021): 14–15. http://dx.doi.org/10.1145/3485952.3485955.

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Nachiappan (Nachi) Nagappan is one of the five ACM Fellows of the 2020 cohort who are also active SIGSOFT members. To celebrate his award, we invited him to a question/answer session. Nachi earned his PhD from North Carolina State University (NCSU), USA, in 2005. Afterwards he was with Microsoft Research for more than 15 years, rising through the ranks from Software Engineer to Partner Researcher. In January 2021, he started in a new position with Facebook. Nachi's research interests are in the field of Software Engineering focusing on Software Reliability, Developer Productivity, Software Dat

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37

Čičin-Šain, M. "Methods for monitoring productivity in applicative software development." Annual Review in Automatic Programming 14 (January 1988): 59–62. http://dx.doi.org/10.1016/0066-4138(90)90011-f.

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38

Dzhalila, Dzhillan, Daniel Siahaan, Reza Fauzan, Raka Asyrofi, and Muhammad Ihsan Karimi. "A Systematic Literature Review on Blockchain Technology in Software Engineering." Jurnal ELTIKOM 7, no. 1 (2023): 38–49. http://dx.doi.org/10.31961/eltikom.v7i1.725.

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Blockchain technology is gaining increasing interest among software developers as a distributed and decentralized ledger for tracking the origin of digital assets. However, the application of blockchain in software engineering requires further attention. In this study, we aim to address the current challenges and explore the need for specialized blockchain practices in software engineering. Through a systematic literature review, we identify the various applications of blockchain technology in software engineering. Additionally, we conduct a thorough analysis of existing obstacles and propose

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39

Arsha Sheikhi, Hamzah Raoof, Zaid Khan, and Merve Gokgol. "Possible Productivity Effects On Software Engineers by Advanced Artificial Intelligence." Proceedings of London International Conferences, no. 11 (November 10, 2024): abs2. http://dx.doi.org/10.31039/plic.2024.11.265.

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Within the past few years, starting from the greater public use of AI from the recent “AI Boom,” ChatGPT or AI-Language Model equivalents have been making their way into software and other computer science-related work environments for developers and software engineers to use without significant financial cost. In this paper, we often mention the word “productivity,” so it is important to know how we measure this: we measure productivity in lines of code (LOC) to gauge the raw amount of coding done, bug resolutions done by developers to measure the reviewing of code, and customer satisfaction

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40

HURLEY, W. DAVID. "A TOOL FOR CONSTRUCTING INTERACTIVE SOFTWARE." International Journal of Software Engineering and Knowledge Engineering 01, no. 01 (1991): 75–98. http://dx.doi.org/10.1142/s0218194091000081.

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The Tailor is a research tool for refining and evaluating a design representation for software systems that have a high degree of textual and graphical interaction. The design representation incorporates both software engineering and knowledge engineering techniques, providing a set of conventions for describing software design information and software construction knowledge. The Tailor demonstrates that the design representation, when embedded in a productivity tool, overcomes limitations of current generation user interface tools. For example, the Tailor has the capability to foresee constru

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41

Cho, Yong Won, Dae Sik Kim, Huy Tung Phuong, and Gwangyong Gim. "The Factors Affecting Continuous Usage Intention of Computer-Aided Engineering (CAE) Software." International Journal of Software Innovation 10, no. 1 (2022): 1–13. http://dx.doi.org/10.4018/ijsi.297508.

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The topic of improving manufacturing productivity has been constantly improving over a long period of time to compromise with unsatisfactory themes such as time, cost, and quality. It has recently faced a whole new challenge of pandemic as well as the various themes of the Fourth Industrial Revolution. At this point in time verifying the intent of continuing use to experts on Computer Engineering Simulation (CAE), one of the main topics of engineering areas for improving manufacturing productivity, is meaningful in determining the direction of future changes. To this end, CAE experts from manu

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42

Fissel, Jim, and Ron Crea. "Applying Human Factors Principles to Complex Software Systems." Proceedings of the Human Factors Society Annual Meeting 32, no. 5 (1988): 267–70. http://dx.doi.org/10.1177/154193128803200504.

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Over the past four years, the User Systems Engineering Group and the Industrial Systems Division of Texas Instruments have worked jointly on designing and developing a computer aided software engineering (CASE) tool to enhance the productivity of process and control engineers. This paper discusses the analysis and design procedures employed during this project and how these procedures focused on customer needs. Specifically, the areas addressed are the multidisciplinary design team, the task analysis, minimal-constraint analysis, and prototype design and iteration.

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URBAN, JOSEPH E., and PATRICK O. BOBBIE. "THE IMPACT OF UNDERGRADUATE SOFTWARE ENGINEERING EDUCATION ON ADVANCING CASE TOOLS." International Journal of Software Engineering and Knowledge Engineering 02, no. 02 (1992): 263–76. http://dx.doi.org/10.1142/s0218194092000130.

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The fundamental rationale for the increase in the use of CASE tools by both large and medium enterprises is the belief that CASE tools facilitate and enhance improved productivity and system quality. The development of CASE environments has evolved over several years. Users are demanding high-level, domain-specific interfaces to applications, easy-to-use systems, systems that offer increases in productivity/cost ratios, flexibility in multiparadigm tools usage, and systems which are modular, portable, and robust. To meet such far ranging needs, software engineering research has become a large-

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BOONE, GREG. "CASE AND ITS CHALLENGE FOR CHANGE." International Journal of Software Engineering and Knowledge Engineering 01, no. 02 (1991): 151–63. http://dx.doi.org/10.1142/s0218194091000147.

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Although the majority of professional trade press and academic attention regarding CASE (Computer Aided Software/Systems Engineering) has focused on technology, software developers have not been deluded by overinflated productivity gains attributed to those technologies. Truly profound technologies require a concomitant change in methods, practices, and techniques. Unfortunately, the majority of the software industry has had the expectation that CASE will automate their current work without rethinking work practices. Changing work practices, particularly among highly independent-minded softwar

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45

Forsgren, Nicole, Margaret-Anne Storey, Chandra Maddila, Thomas Zimmermann, Brian Houck, and Jenna Butler. "The SPACE of Developer Productivity." Queue 19, no. 1 (2021): 20–48. http://dx.doi.org/10.1145/3454122.3454124.

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Developer productivity is about more than an individual's activity levels or the efficiency of the engineering systems relied on to ship software, and it cannot be measured by a single metric or dimension. The SPACE framework captures different dimensions of productivity, and here we demonstrate how this framework can be used to understand productivity in practice and why using it will help teams better understand developer productivity and create better measures to inform their work and teams.

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46

Newton, Olivia B., Stephen M. Fiore, and Jihye Song. "Expertise and Complexity as Mediators of Knowledge Loss in Open Source Software Development." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 1580–84. http://dx.doi.org/10.1177/1071181319631445.

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This paper describes an approach integrating cognitive engineering with computational social science in the context of open source software (OSS) development. Through an analysis of large-scale collaborations in a complex operational setting, we study how expertise and task complexity predict changes in productivity when knowledge loss occurs. Using team data from thousands of software files, we model the effects of expertise, complexity, and knowledge loss on productivity. On its own, knowledge loss had a negative effect on productivity, but this effect was reversed when knowledge loss was co

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47

Ventura-Molina, Elías, Cuauhtémoc López-Martín, Itzamá López-Yáñez, and Cornelio Yáñez-Márquez. "A Novel Data Analytics Method for Predicting the Delivery Speed of Software Enhancement Projects." Mathematics 8, no. 11 (2020): 2002. http://dx.doi.org/10.3390/math8112002.

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A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as suc

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48

Jasman, Jasman. "The Impact of Low-Code and No-Code Development on IT Workforces and Software Engineering Practices in Indonesia." West Science Information System and Technology 2, no. 03 (2024): 413–18. https://doi.org/10.58812/wsist.v2i03.1815.

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This study examines the impact of low-code and no-code (LCNC) development platforms on IT workforces and software engineering practices in Indonesia. Employing a quantitative approach with 50 respondents and data analysis using SPSS version 25, the research investigates the relationships between LCNC adoption and variables such as workforce productivity, skill adaptability, software quality, and development efficiency. The findings indicate that LCNC platforms significantly enhance workforce productivity and development efficiency, with moderate impacts on skill adaptability and mixed percepti

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Kuldeep Deshwal. "Developer Productivity: Navigating Common Pitfalls in Modern Engineering Organizations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 14–23. https://doi.org/10.32628/cseit25112865.

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Developer productivity initiatives often backfire when organizations focus solely on output metrics without considering the human elements of software creation. This article examines common pitfalls that undermine developer effectiveness, including metric obsession, micromanagement, context-switching, ambiguous expectations, inadequate tooling, technical debt neglect, collaboration silos, deadline-driven development, feedback deprivation, and neglected well-being. By identifying these counterproductive patterns, engineering leaders can instead build sustainable productivity models centered on

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Deepa T, Dr. Thaddues S. "Productivity and Quality Challenges of Novice Developers in Agile Engineering Environments - A Comparative Study." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 4542–50. http://dx.doi.org/10.52783/tjjpt.v44.i4.1742.

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The Agile methodology has significantly transformed the landscape of software development, offering a dynamic approach that prioritizes adaptability and customer feedback. This paper harnesses insights from the authoritative State of Agile report, an annual publication produced by CollabNet and Digital.ai. Findings of six financial years (2015-2021) reveal the current positioning of the software development community in terms of agile adoption and the prevalent trends. This finding was elaborated with a micro survey, conducted targeting various agile roles within software development companies

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