Replication studies in Software Engineering are indispensable for ensuring the reliability, generalizability, and transparency of research findings. They contribute to the cumulative growth of knowledge in the field and promote a scientific approach that benefits both researchers and practitioners. In this article, we report our experience replicating a recently published work proposing a Transformer-based approach for Agile Story Point Estimation” dubbed GPT2SP. GPT2SP was proposed with the intent of addressing the three limitations of a previous Deep Learning-based approach dubbed Deep-SE, and the results reported in the original study set GPT2SP as the new state-of-the-art. However, when we used the GPT2SP source code made publicly available by the authors of the original study, we found a bug in the computation of the evaluation measure and the reuse of erroneous results from previous work, which had unintentionally introduced biases in the GPT2SP's performance evaluation. In this study, we report on the results we obtained after fixing the issues present in the original study, which reveal that their results were in fact unintentionally inflated due to these issues and that despite advancements, challenges remain in providing accurate effort estimations for agile software projects.

In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates.

Background: Previous work has provided some initial evidence that Story Point (SP) estimated by human-experts may not accurately reflect the effort needed to realise Agile software projects. Aims: In this paper, we aim to shed further light on the relationship between SP and Agile software development effort to understand the extent to which human-estimated SP is a good indicator of user story development effort expressed in terms of time needed to realise it. Method: To this end, we carry out a thorough empirical study involving a total of 37,440 unique user stories from 37 different open-source projects publicly available in the TAWOS dataset. For these user stories, we investigate the correlation between the issue development time (or its approximation when the actual time is not available) and the SP estimated by human-expert by using three widely-used correlation statistics (i.e., Pearson, Kendall and Spearman). Furthermore, we investigate SP estimations made by the human-experts in order to assess the extent to which they are consistent in their estimations throughout the project, i.e., we assess whether the development time of the issues is proportionate to the SP assigned to them. Results: The average results across the three correlation measures reveal that the correlation between the human-expert estimated SP and the approximated development time is strong for only 7% of the projects investigated, and medium (58%) or low (35%) for the remaining ones. Similar results are obtained when the actual development time is considered. Our empirical study also reveals that the estimation made is often not consistent throughout the project and the human estimator tends to misestimate in 78% of the cases. Conclusions: Our empirical results suggest that SP might not be an accurate indicator of open-source Agile software development effort expressed in terms of development time. The impact of its use as an indicator of effort should be explored in future work, for example as a cost-driver in automated effort estimation models or as the prediction target.

Agile software development is nowadays a widely adopted practise in both open-source and industrial software projects. Agile teams typically heavily rely on issue management tools to document new issues and keep track of outstanding ones, in addition to storing their technical details, effort estimates, assignment to developers, and more. Previous work utilised the historical information stored in issue management systems for various purposes; however, when researchers make their empirical data public, it is usually relevant solely to the study's objective. In this paper, we present a more holistic and versatile dataset containing a wealth of information on more than half a million issues from 44 open-source Agile software, making it well-suited to several research avenues, and cross-analyses therein, including effort estimation, issue prioritization, issue assignment and many more. We make this data publicly available on GitHub to facilitate ease of use, maintenance, and extensibility.

Automated techniques to estimate Story Points (SP) for user stories in agile software development came to the fore a decade ago. Yet, the state-of-the-art estimation techniques' accuracy has room for improvement. In this paper, we present a new approach for SP estimation, based on analysing textual features of software issues by employing latent Dirichlet allocation (LDA) and clustering. We first use LDA to represent issue reports in a new space of generated topics. We then use hierarchical clustering to agglomerate issues into clusters based on their topic similarities. Next, we build estimation models using the issues in each cluster. Then, we find the closest cluster to the new coming issue and use the model from that cluster to estimate the SP. Our approach is evaluated on a dataset of 26 open source projects with a total of 31,960 issues and compared against both baselines and state-of-the-art SP estimation techniques. The results show that the estimation performance of our proposed approach is as good as the state-of-the-art. However, none of these approaches is statistically significantly better than more naive estimators in all cases, which does not justify their additional complexity. We therefore encourage future work to develop alternative strategies for story points estimation. The experimental data and scripts we used in this work are publicly available to allow for replication and extension.

Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly ($p <0.001$); (2) CoGEE’s multi-objective nature makes it able to reach such a good performance; (3) CoGEE’s estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8 percent with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our work.