Aug 2026· Comprehensive Journal of Science· 0 citations· 8 references
TL;DR
According to the results, deep learning can be more beneficial for forecasting technical debt but does not bring the required level of improvement only due to the complexity of the model.
Abstract
Technical debt (TD) is an everlasting issue in software engineering. This is the issue of short-term programming choices leading to larger costs in terms of maintenance, quality, and evolution in the future. This issue becomes critically important for large software projects, as they usually contain various quality indicators and constantly changing states of the system. The goal of the study is to create and evaluate an efficient intelligent model for forecasting technical debt. The experiment utilized the Technical Debt Forecasting dataset available on Zenodo. The dataset has been gathered from 15 open source programming projects. The compiled dataset is formed by 1,918 measurements taken during the process of the software under consideration. The dataset consists of 41 common predictive variables. The report uses 70/15/15 temporal split to avoid time leakage. The TCN utilizes an eight-step temporal window and outperforms persistence, Random Forest, and Gradient Boost models.
The predictions of normalized technical debt for the second step based on TCN in the within-project test observations (n = 172) yielded MAE = 0.001388, RMSE = 0.002956, and R² = 0.999842. The results indicated that on this dataset, the persistence method achieved better scores of MAE = 0.001166, RMSE = 0.002943, and R² = 0.999843. To analyze what the differences in results are in terms of statistical significance, a Wilcoxon signed-rank test was applied to compare paired absolute errors, and the computed differences turned out to be statistically significant (p < .001) favoring the persistence model. The TCN was also used for getting results on cross-project data with Groovy, Kafka, and CommonsIO being excluded. The results there demonstrated MAE = 0.003930 for TCN compared to 0.003110 for persistence. According to the results, deep learning can be more beneficial for forecasting technical debt but does not bring the required level of improvement only due to the complexity of the model.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components -- positive and negative emotions and moods -- is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· Journal of Systems and Softw...· 236 citations· ⚡13
Mobile phones have been closed environments until recent years. The change brought by open platform technologies such as the Symbian operating system and Java technologies has opened up a significant business opportunity for anyone to develop application software such as games for mobile terminals. However, developing mobile applications is currently a challenging task due to the specific demands and technical constraints of mobile development. Furthermore, at the moment very little is known about the suitability of the different development processes for mobile application development. Due to these issues, we have developed an agile development approach called Mobile-D. The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed.
P. Abrahamsson, Antti Hanhineva, H. Hulkko et al.· Conference on Object-Oriente...· 225 citations· ⚡18
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