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Journal articles on the topic 'Software Development Productivity'

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Published: 4 June 2025
Last updated: 23 June 2025

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1

Maxwell, K. D., and P. Forselius. "Benchmarking software development productivity." IEEE Software 17, no. 1 (2000): 80–88. http://dx.doi.org/10.1109/52.820015.

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2

Kemerer, Chris F. "Software development productivity measurement." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 17, no. 4 (1986): 41. http://dx.doi.org/10.1145/1113523.1113533.

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3

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 global distribution of expert work increases the degree of difficulty. In this paper, the authors analyze multicultural ICT companies from their productivity perspective through the lens of cultural differences. The purpose of this study is to report findings based on general cultural studies and reported experiences that seem to affect productivity in the software industry. Some company cases are also described and analyzed.
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4

Sudhakar, Purna, Ayesha Farooq, and Sanghamitra Patnaik. "Measuring productivity of software development teams." Serbian Journal of Management 7, no. 1 (2012): 65–75. http://dx.doi.org/10.5937/sjm1201065s.

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5

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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6

Gorla, Narasimhaiah, and Ravi Ramakrishnan. "Effect of software structure attributes on software development productivity." Journal of Systems and Software 36, no. 2 (1997): 191–99. http://dx.doi.org/10.1016/0164-1212(95)00202-2.

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7

ZHAN, JIZHOU, XIANZHONG ZHOU, and JIABAO ZHAO. "IMPACT OF SOFTWARE COMPLEXITY ON DEVELOPMENT PRODUCTIVITY." International Journal of Software Engineering and Knowledge Engineering 22, no. 08 (2012): 1103–22. http://dx.doi.org/10.1142/s0218194012500301.

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With increasing demands on software functions, software systems become more and more complex. This complexity is one of the most pervasive factors affecting software development productivity. Assessing the impact of software complexity on development productivity helps to provide effective strategies for development process and project management. Previous research literatures have suggested that development productivity declines exponentially with software complexity. Borrowing insights from cognitive learning psychology and behavior theory, the relationship between software complexity and development productivity was reexamined in this paper. This research identified that the relationship partially showed a U-shaped as well as an inverted U-shaped curvilinear tendency. Furthermore, the range of complexity level that is beneficial for productivity has been presented, in which, the lower bound denotes the minimum degree of complexity at which personnel can be motivated, while the upper bound shows the maximum extent of complexity that staff can endure. Based on our findings, some guidelines for improving personnel management of software industry have also been given.
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8

Alavi, Maryam. "High-Productivity Alternatives for Applications Software Development." Journal of Information Systems Management 2, no. 4 (1985): 19–24. http://dx.doi.org/10.1080/07399018508967781.

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9

Tsunoda, Masateru, Akito Monden, Hiroshi Yadohisa, Nahomi Kikuchi, and Kenichi Matsumoto. "Software development productivity of Japanese enterprise applications." Information Technology and Management 10, no. 4 (2009): 193–205. http://dx.doi.org/10.1007/s10799-009-0050-9.

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10

Mota, Jhemeson Silva, Heloise Acco Tives, and Edna Dias Canedo. "Tool for Measuring Productivity in Software Development Teams." Information 12, no. 10 (2021): 396. http://dx.doi.org/10.3390/info12100396.

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Despite efforts to define productivity, there is no consensus in the software industry regarding what the term productivity means and, instead of having only one metric or factor that describes productivity, it is defined by a set of aspects. Our objective is to develop a tool that supports the productivity measurement of software development teams according to the factors found in the literature. We divided these factors into four groups: People, Product, Organization, and Open Source Software Projects. We developed a web system containing the factors that influence productivity identified in this work, called Productive, to support software development teams in measuring their productivity. After developed the tool, we monitored its use over eight weeks with two small software development teams. From the results, we found that software development companies can use the system to support monitoring team productivity. The results also point to an improvement in productivity while using the system, and a survey applied to users demonstrates the users’ positive perception regarding the results obtained. In future work, we will monitor the use of the tool and investigate the users’ perceptions in other project contexts.
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11

Ondrej, Machek, Hnilica Jiri, and Hejda Jan. "Estimating Productivity of Software Development Using the Total Factor Productivity Approach." International Journal of Engineering Business Management 4 (January 2012): 34. http://dx.doi.org/10.5772/52797.

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12

Iqbal, Javed, Azman Yasin, and Mazni Omar. "Defining Teamwork Productivity Factors in Agile Software Development." International Journal on Advanced Science, Engineering and Information Technology 12, no. 3 (2022): 1160. http://dx.doi.org/10.18517/ijaseit.12.3.13648.

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13

Maxwell, K. D. "Collecting data for comparability: benchmarking software development productivity." IEEE Software 18, no. 5 (2001): 22–25. http://dx.doi.org/10.1109/52.951490.

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14

Rahman, Abdul, Eko Indrajit, Akhmad Unggul, and Erick Dazki. "Agile Project Management Impacts Software Development Team Productivity." sinkron 8, no. 3 (2024): 1847–58. http://dx.doi.org/10.33395/sinkron.v8i3.13853.

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The agile nature of the software development sector calls for flexible and effective project management techniques. Agile Project Management (APM) is emerging as a significant method that supports team cooperation, iterative improvement, and flexibility. This paper looks at how agile project management might affect software development team output. This study investigates the primary Agile methodologies Scrum and their impact on team productivity by means of a thorough literature review and empirical analysis. A mixed-methods approach employs qualitative comments and quantitative measures to provide a comprehensive view of output changes. We examine several software development teams inside a mid-sized technology company over 12 months using a case study approach, comparing productivity measures before and after Agile practices, including team satisfaction, development pace, and code quality. Furthermore, team member surveys and interviews offer an understanding of the supposed advantages and difficulties of switching to Agile approaches. Teams showing more efficiency, improved communication, and better morale point to a notable rise in productivity. Notable improvements included improved adaptability to shifting project needs and a shorter time-to-market for software products. This paper offers an insightful analysis of Agile Project Management's ability to revolutionize software development processes, helping companies trying to improve project results. This study has consequences for managers and practitioners because it provides valuable instructions for implementing Agile approaches to achieve the best team performance. Future directions of study will include investigating the long-term effects of Agile methods and their relevance in various organizational settings.
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15

Flitman, Andrew. "Towards meaningful benchmarking of software development team productivity." Benchmarking: An International Journal 10, no. 4 (2003): 382–99. http://dx.doi.org/10.1108/146357703104484999.

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16

Lagerström, Robert, Liv Marcks von Würtemberg, Hannes Holm, and Oscar Luczak. "Identifying factors affecting software development cost and productivity." Software Quality Journal 20, no. 2 (2011): 395–417. http://dx.doi.org/10.1007/s11219-011-9137-8.

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17

Čičin-Šain, M. "Methods for Monitoring Productivity in Applicative Software Development." IFAC Proceedings Volumes 21, no. 14 (1988): 59–62. http://dx.doi.org/10.1016/s1474-6670(17)53682-4.

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18

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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19

Rodger, James A., Pankaj Pankaj, and Ata Nahouraii. "Knowledge Management of Software Productivity and Development Time." Journal of Software Engineering and Applications 04, no. 11 (2011): 609–18. http://dx.doi.org/10.4236/jsea.2011.411072.

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20

Mohamed, Samer I. "Software development productivity impact from an industrial perspective." International Journal of Scientific & Engineering Research 6, no. 2 (2015): 1333–42. http://dx.doi.org/10.14299/ijser.2015.02.007.

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21

Low, GC, and DR Jeffery. "Software development productivity and back-end CASE tools." Information and Software Technology 33, no. 9 (1991): 616–21. http://dx.doi.org/10.1016/0950-5849(91)90033-8.

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22

Gustafson, Shireen. "The development of customized software for productivity improvement." Computers & Industrial Engineering 17, no. 1-4 (1989): 159–63. http://dx.doi.org/10.1016/0360-8352(89)90054-5.

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23

Jeffery, D. R. "A software development productivity model for MIS environments." Journal of Systems and Software 7, no. 2 (1987): 115–25. http://dx.doi.org/10.1016/0164-1212(87)90016-1.

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24

Dalcher, Darren. "Supporting software development: enhancing productivity, management and control." Software Process: Improvement and Practice 11, no. 6 (2006): 557–59. http://dx.doi.org/10.1002/spip.317.

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25

Varajão, João, António Trigo, and Miguel Almeida. "Low-code Development Productivity." Queue 21, no. 5 (2023): 87–107. http://dx.doi.org/10.1145/3631183.

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This article aims to provide new insights on the subject by presenting the results of laboratory experiments carried out with code-based, low-code, and extreme low-code technologies to study differences in productivity. Low-code technologies have clearly shown higher levels of productivity, providing strong arguments for low-code to dominate the software development mainstream in the short/medium term. The article reports the procedure and protocols, results, limitations, and opportunities for future research.
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26

Guerrero-Calvache, Sandra-Marcela, and Giovanni Hernández. "Conceptions and Perceptions of Software Industry Professionals on Team Productivity in Agile Software Development: A Comparative Study." Revista Facultad de Ingeniería 30, no. 58 (2021): e13817. http://dx.doi.org/10.19053/01211129.v30.n58.2021.13817.

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Agile software development (ASD) has generated different benefits in organizations and in the Software Industry, mainly in improving productivity. For ASD teams this indicator plays a fundamental role since it helps determine their performance. However, evaluating productivity is a great challenge and the way in which this concept has been approached in the literature is very limited. The objective of this article is to contrast the conceptions of productivity at the team level from an ASD perspective with the perceptions that professionals in the software industry have. For the methodological design, the notions of team productivity presented in the literature were identified and compared with the perceptions of 72 professionals from the software industry collected through a survey following the protocol proposed by Kitchenham and Pfleeger. The main results show that the concept of team productivity in the literature is associated with a set of dimensions related to satisfaction, delivery of functional software, and knowledge transfer. On the part of the respondents, a perception of general productivity centered on dimensions of customer satisfaction, activity management, and early identification of the problem to be solved is evidenced. It can be concluded that the professionals' imaginaries focus on presenting productivity from a generic perspective and its dimensions do not necessarily involve teamwork.
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27

Yilmaz, Murat, Rory V. O'Connor, and Paul Clarke. "Effective Social Productivity Measurements during Software Development — An Empirical Study." International Journal of Software Engineering and Knowledge Engineering 26, no. 03 (2016): 457–90. http://dx.doi.org/10.1142/s0218194016500194.

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Much of contemporary scientific discussion regarding factors that influence software development productivity is undertaken in various domains where there is an insufficient empirical basis for exploring socio-technical factors of productivity that are specific to a software development organization. The purpose of the study is to characterize the multidimensional nature of software development productivity and its social aspects as a set of latent constructs (i.e. variables that are not directly observed) for a medium-sized software company. To this end, we designed an exploratory in-depth field study based on the hypothesized productivity constructs, which were modeled by a set of factors identified from literature reviews, and later refined by industrial focus groups. In order to demonstrate the applicability of our approach, we conducted confirmatory factor analysis with the data attained from a questionnaire with 216 participants. To investigate factors of influence further, we analyzed the impact of selected team-based variables over the latent constructs of productivity. Taken together, our findings confirm that such an approach can be used to explore the quantifiable influence of socio-technical factors that would affect productivity of a particular software development organization. Ultimately, the resulting model provides guidance to explore the comparative importance of a set of firm-specific factors that may help to improve the productivity of the organization.
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28

Radliński, Łukasz. "Analysis of factors of software development effort and productivity." Procedia Computer Science 192 (2021): 4790–99. http://dx.doi.org/10.1016/j.procs.2021.09.257.

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29

Pai, Dinesh R., Girish H. Subramanian, and Parag C. Pendharkar. "Benchmarking software development productivity of CMMI level 5 projects." Information Technology and Management 16, no. 3 (2015): 235–51. http://dx.doi.org/10.1007/s10799-015-0234-4.

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30

Asmild, Mette, Joseph C. Paradi, and Atin Kulkarni. "Using Data Envelopment Analysis in software development productivity measurement." Software Process: Improvement and Practice 11, no. 6 (2006): 561–72. http://dx.doi.org/10.1002/spip.298.

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31

Jørgensen, Magne. "Characteristics and generative mechanisms of software development productivity distributions." Information and Software Technology 159 (July 2023): 107215. http://dx.doi.org/10.1016/j.infsof.2023.107215.

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32

Suzuki, Hikaru. "Improving Productivity of Software Development on the Macchinetta Framework." NTT Technical Review 15, no. 2 (2017): 1–3. http://dx.doi.org/10.53829/ntr201702fa1.

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33

Tyumentsev, E. A. "On the formalization of the software development process." Mathematical structures and modeling, no. 3 (2017): 96–107. http://dx.doi.org/10.24147/2222-8772.2017.3.96-107.

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In his book, The Mythical Man-Month, or How Software Systems Are Built, Brooks cites several studies that suggest that programmer productivity, measured in lines of code per unit of time, declines as the size of a software project grows. This paper describes the formalization of the software development process as a process of editing the source code of a program, which is used to determine the labor intensity function, and also derives a sufficient condition for constant programmer productivity, independent of the size of the project.
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34

Liao, Zhifang, Xiaofei Qi, Yan Zhang, Xiaoping Fan, and Yun Zhou. "How to Evaluate the Productivity of Software Ecosystem: A Case Study in GitHub." Scientific Programming 2020 (August 3, 2020): 1–13. http://dx.doi.org/10.1155/2020/8814247.

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With the development of open source community, the software ecosystem has become a popular perspective in the research on software development process and environment. Software productivity is an important evaluation indicator of the software ecosystem health. A successful software ecosystem relies on long-term and stable production activities by the users, which ensures that the software ecosystem can continuously provide the value needed by users. Therefore, the measurement of software ecosystem productivity can help maintain the user development efficiency and the stability of the software ecosystem. However, there is still little literature on the productivity of open source software ecosystems. By analogy with the natural ecosystem, this paper gives the relevant definitions of software ecosystem productivity and analyzes the factors affecting the productivity of software ecosystem. Based on the factors of the ecosystem productivity and their interrelationships, this paper establishes a software ecosystem productivity model and takes the GitHub platform as an example for detailed analysis and explanation. The results show that the model can better explain the factors affecting the productivity of software ecosystems. It is helpful for the research on the measurement of the software ecosystem health and the software development efficiency.
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35

Blackburn, J. D., G. D. Scudder, and L. N. Van Wassenhove. "Improving speed and productivity of software development: a global survey of software developers." IEEE Transactions on Software Engineering 22, no. 12 (1996): 875–85. http://dx.doi.org/10.1109/32.553636.

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36

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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37

Jaiswal, Kapil, and Minakshi Garg. "Exploring Relationship Between Tqm and Software Productivity." Ingeniería Solidaria 15, no. 29 (2019): 1–29. http://dx.doi.org/10.16925/2357-6014.2019.03.09.

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Introduction: This publication is the product of research, carried out in the field of management in year 2018-19, which supports the work of a PhD in Business Management at Chandigarh University. The purpose of this research is to explore the relation between Total Quality Management (TQM) constructs and productivity in the IT industry. This study has been conducted for organizations operating in the Tricity (Chandigarh, Panchkula and Mohali) and NCR (Noida, Gurgaon and Delhi) regions.
 Problem: The control of rising operational costs in any organization has become a challenge and is a major aspect in the sustainability of an organization. Implementation of TQM may reduce these costs by improving productivity in the software development process.
 Objective: The objective of the research is to explore if there any relationship exists between TQM and productivity in software development organization and whether TQM positively impacts productivity.
 Methodology: The study is based on a descriptive research design. A total of 206 respondents were selected using convenient sampling while 90 responded back on the survey. Exploratory factor Analysis and Multiple Linear Regression techniques were applied to obtain the results.
 Results: Out of 4 elements of TQM considered in this study, Customer Focus and Continuous improvement were found to be positively related to productivity while Total Management Commitment was found to not be related to productivity. The hypothesis related to People Management was abandoned because it was highly correlated to other TQM elements.
 Conclusion: TQM positively impacts productivity in software development organizations.
 Originality: This study tried to create a causal mathematical model between TQM variables and productivity.
 Limitations: Sample size and TQM elements were limited based on availability of time and resources.
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38

Bhardwaj, Mridul, and Ajay Rana. "Key Software Metrics and its Impact on each other for Software Development Projects." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 242. http://dx.doi.org/10.11591/ijece.v6i1.8247.

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<p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"><span style="line-height: 115%; font-size: 9pt; mso-bidi-font-size: 12.0pt;"><span style="font-family: Calibri;"><span style="mso-spacerun: yes;"><span style="font-size: small;"><span style="line-height: 115%; mso-bidi-font-size: 12.0pt;">Every software development project is unique and different from repeatable manufacturing process. Each software project share different challenges related to technology, people and timelines. If every project is unique, how project manager can estimate project in a consistent way by applying his past experience. One of the major challenges faced by the project manager is to identify the key software metrics to control and monitor the project execution. Each software development project may be unique but share some common metric that can be used to control and monitor the project execution. These metrics are software size, effort, project duration and productivity. These metrics tells project manager about what to deliver (size), how it was delivered in past (productivity) and how long will it take to deliver with current team capability (time and effort). In this paper, we explain the relationship among these key metrics and how they statistically impact each other. These relationships have been derived based on the data published in book “Practical Software Estimation” by International Software Benchmarking Group. This paper also explains how these metrics can be used in predicting the total number of defects. Study suggests that out of the four key software metrics software size significantly impact the other three metrics (project effort, duration and productivity). Productivity does not significantly depend on the software size but it represents the nonlinear relationship with software size and maximum team size, hence, it is recommended not to have a very big team size as it might impact the overall productivity. Total project duration only depends on the software size and it does not depend on the maximum team size. It implies that we cannot reduce project duration by increasing the team size. This fact is contrary to the perception that we can reduce the project duration by increasing the project team size. We can conclude that software size is the important metrics and a significant effort must be put during project initiation phases to estimate the project size. As software size will help in estimating the project duration and project efforts so error in estimating the software size will have significant impact on the accuracy of project duration and effort. All these key metrics must be re-calibrated during the project development life cycle. </span><strong style="mso-bidi-font-weight: normal;"></strong></span></span></span></span></p><p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"> </p>
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39

Bhardwaj, Mridul, and Ajay Rana. "Key Software Metrics and its Impact on each other for Software Development Projects." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 242. http://dx.doi.org/10.11591/ijece.v6i1.pp242-248.

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<p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"><span style="line-height: 115%; font-size: 9pt; mso-bidi-font-size: 12.0pt;"><span style="font-family: Calibri;"><span style="mso-spacerun: yes;"><span style="font-size: small;"><span style="line-height: 115%; mso-bidi-font-size: 12.0pt;">Every software development project is unique and different from repeatable manufacturing process. Each software project share different challenges related to technology, people and timelines. If every project is unique, how project manager can estimate project in a consistent way by applying his past experience. One of the major challenges faced by the project manager is to identify the key software metrics to control and monitor the project execution. Each software development project may be unique but share some common metric that can be used to control and monitor the project execution. These metrics are software size, effort, project duration and productivity. These metrics tells project manager about what to deliver (size), how it was delivered in past (productivity) and how long will it take to deliver with current team capability (time and effort). In this paper, we explain the relationship among these key metrics and how they statistically impact each other. These relationships have been derived based on the data published in book “Practical Software Estimation” by International Software Benchmarking Group. This paper also explains how these metrics can be used in predicting the total number of defects. Study suggests that out of the four key software metrics software size significantly impact the other three metrics (project effort, duration and productivity). Productivity does not significantly depend on the software size but it represents the nonlinear relationship with software size and maximum team size, hence, it is recommended not to have a very big team size as it might impact the overall productivity. Total project duration only depends on the software size and it does not depend on the maximum team size. It implies that we cannot reduce project duration by increasing the team size. This fact is contrary to the perception that we can reduce the project duration by increasing the project team size. We can conclude that software size is the important metrics and a significant effort must be put during project initiation phases to estimate the project size. As software size will help in estimating the project duration and project efforts so error in estimating the software size will have significant impact on the accuracy of project duration and effort. All these key metrics must be re-calibrated during the project development life cycle. </span><strong style="mso-bidi-font-weight: normal;"></strong></span></span></span></span></p><p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"> </p>
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40

Yilmaz, Murat, and Rory O’Connor. "Social Capital as a Determinant Factor of Software Development Productivity." International Journal of Human Capital and Information Technology Professionals 3, no. 2 (2012): 40–62. http://dx.doi.org/10.4018/jhcitp.2012040104.

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Social capital is an important network based intangible asset with a potential for maximizing individual and team productivity in a social setting like software development. It is important to investigate intervening factors that challenge software development productivity. In this paper, the authors mixed method approach harnesses a structural equation model (SEM) for its quantitative part to establish a paradigm for understanding the effects of social factors for software development organizations. The proposed SEM model measures the correlations between several potential factors associated with productivity, social productivity, and social capital that are chosen as latent variables. For the qualitative phase, an industrial focus group is used to single out these factors and their association with potential social aspects. Quantitative data is gathered from a survey conducted at a university. The qualitative phase encompasses an industrial focus group, initially starting with the factors from the literature and refined through participants’ field experience. Findings indicate that a high correlation exists between several social factors that are reported by the focus group. Finally, initial results suggest that understanding the factors that affect social capital in software development is essential for building and sustaining highly productive development environments.
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41

Kumar Nath, Udit, Satyasundara Mahapatra, Prasant Kumar Pattnaik, and Alok Kumar Jagadev. "Issues of lean-agile software development environment." International Journal of Engineering & Technology 7, no. 3.3 (2018): 432. http://dx.doi.org/10.14419/ijet.v7i2.33.14204.

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The popular waterfall model is widely accepted approach for project management paradigm; however lean based agile model is the recent revolution to reduce work in progress items and makes transformation to better process by identifying and eliminating non-value-add activities and increase productivity with quality of deliverables. This paper includes the issues that involved in lean- agile process.
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42

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 assistants built on large language models demonstrate increasingly sophisticated capabilities in code generation, context understanding, and optimization suggestions across multiple programming languages. These technologies serve as productivity multipliers and knowledge equalizers within development teams, enabling significant reductions in routine task completion time while allowing developers to focus on higher-value creative and architectural activities. Despite these benefits, important challenges persist, including technical constraints, developer dependency concerns, intellectual property uncertainties, and privacy considerations. As AI continues to reshape the software engineering landscape, organizations, educational institutions, and individual practitioners must carefully navigate these evolving dynamics to maximize benefits while mitigating potential drawbacks.
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43

Wiesche, Manuel. "Interruptions in Agile Software Development Teams." Project Management Journal 52, no. 2 (2021): 210–22. http://dx.doi.org/10.1177/8756972821991365.

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Abstract:
Agile approaches help software development project teams to better meet user needs and ensure flexibility in uncertain environments. But using agile approaches invites changes to the project and increases interactions between team members, which both cause interruptions in the workplace. While interruptions can help in task completion and increase process flexibility, they can also hinder employee productivity. We conducted an exploratory study of four agile software development teams. Our analysis identified (1) programming-related work impediments, (2) interaction-related interruptions, and (3) interruptions imposed by the external environment, which were managed by improved information retrieval and reduced team dependencies.
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44

Lee, Bokyeong, Hyeonggil Choi, Byongwang Min, Jungrim Ryu, and Dong-Eun Lee. "Development of formwork automation design software for improving construction productivity." Automation in Construction 126 (June 2021): 103680. http://dx.doi.org/10.1016/j.autcon.2021.103680.

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45

Asmild, Mette, and Francisco Imperatore. "On the Use of DEA for Software Development Productivity Measurement." Data Envelopment Analysis Journal 2, no. 1 (2016): 81–111. http://dx.doi.org/10.1561/103.00000011.

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46

Mohapatra, Sanjay. "Maximising productivity by controlling influencing factors in commercial software development." International Journal of Information and Communication Technology 3, no. 2 (2011): 160. http://dx.doi.org/10.1504/ijict.2011.041746.

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47

Maxwell, K. D., L. Van Wassenhove, and S. Dutta. "Software development productivity of European space, military, and industrial applications." IEEE Transactions on Software Engineering 22, no. 10 (1996): 706–18. http://dx.doi.org/10.1109/32.544349.

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48

Kalava, Sudheer Peddineni. "AI-Powered Development: How Artificial Intelligence is Shaping Software Productivity." Journal of Artificial Intelligence & Cloud Computing 3, no. 2 (2024): 1–4. http://dx.doi.org/10.47363/jaicc/2024(3)e148.

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49

Flynn, Donal, Julien Vagner, and Olivier Dal Vecchio. "Is CASE technology improving quality and productivity in software development?" Logistics Information Management 8, no. 2 (1995): 8–21. http://dx.doi.org/10.1108/09576059510084966.

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50

Rodríguez, D., M. A. Sicilia, E. García, and R. Harrison. "Empirical findings on team size and productivity in software development." Journal of Systems and Software 85, no. 3 (2012): 562–70. http://dx.doi.org/10.1016/j.jss.2011.09.009.

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