Point Of Story Research Articles

Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts.

The accuracy of bedside attention tests for delirium detection in older outpatients with cognitive impairment

BackgroundTo determine the test accuracy of four observations, three explorations and four short tests commonly used in psychiatric examinations to detect attention disorders related to delirium in older outpatients. MethodWe performed a test accuracy study among 86 older patients referred for cognitive screening to an outpatient clinic of a psychiatric institution. Eleven index tests were applied without knowledge of the reference standard. A geriatrician or psychiatrist blinded to the results of the index tests determined the reference diagnosis with DSM-IV-TR criteria. Observations were lack of adequate/ regular eye-contact, dozing off during conversation, dozing off when not stimulated, and losing the point of story. Explored was lack of concentration during activities, difficulty following conversation, and being easily distracted. Serial 7s, WORLD spelled backward, months of the year backward (MOTYB) and counting back from 20 to 0 were applied. Test accuracy was calculated for the single tests and combinations. ResultsDozing off during conversation scored high specificity (97.9 %) as did dozing off when not stimulated (97.9 %), but both had low sensitivity (< 27.9 %). Test accuracy of the exploration questions was low. Serial 7s and WORLD spelled backwards had high sensitivity (92.7 %, respectively 80.5 %) but low specificity (< 51.1 % %). The best combination was MOTYB with WORLD spelled backward (sensitivity of 63.4 %, specificity of 77.8 %). ConclusionsNo single observation, exploration or short test of attention was found to have both high sensitivity and specificity. Probably, attention deficits in delirium are best detected with a combination of observations and short tests as part of a comprehensive psychiatric examination.

STRUKTUR CERITA RAKYAT JAWA TENGAH BALAI-BALAI KAYU JATI: KAJIAN ANTROPOLOGI SASTRA

Folklore is a story passed down from generation to generation that has been passed down from generation to generation orally to be preserved. Folklore contains aspects relating to attitudes, behavior and cultural values of society. Literary anthropology studies discuss literary works by emphasizing cultural values. This research aims to explain the structure and content of the story of Balai-Balai Kayu Jati and reveal the cultural reflection of Central Javanese society (especially the Tlogosari community). Balai-Balai Kayu Jati are analyzed from two perspectives, namely as works of fiction and as physical evidence of culture. As a work of fiction, a study is carried out on the structural elements of the story, while as physical evidence of culture, it is analyzed using a literary anthropology approach. The research results show that structurally, the story of Balai-Balai Kayu Jati is composed of characters, themes, settings and story points of view. Meanwhile, by using a literary anthropology approach, we can understand the cultural side of the people of Tlogosari, Central Java, namely the aspects of knowledge, technology, social society and art.

The Western Aramaic Context of a Famous Lullaby

Abstract The present text concerns a traditional lullaby, sung throughout the Levant in various versions, of which the most famous was produced by the Rahbani brothers and sung by Fairouz. The framing story surrounding the lullaby, which is only implicit in the Rahbani version, concerns themes such as kidnapping and the age-old conflict between transhumant pastoral groups and sedentary agriculturalists. Despite the perennial widespread popularity of the lullaby, aspects of its framing story point to its origins in Maaloula.

Przestrzeń w grze wideo Dark Souls jako narzędzie do kreowania światoopowieści

&#x0D; &#x0D; &#x0D; The aim of the article is to analyze how space in the video game “Dark Souls” affects various aspects of the interactive text, especially its plot and gameplay. The author focuses on the role of space in the narrative structure of the game, using storyworld concept, analyzing architecture, and the importance of items and their descriptions in constructing the narrative, which is crucial for the reconstruction of story points and also a proper understanding of the game’s religious motives. He goes on to focus on the storyworld events generated by gameplay, describing how it is influenced by space arrangement.&#x0D; &#x0D; &#x0D;

Compiling Requirements from Models for Early Phase Scope Estimation in Agile Software Development Projects

Inadequate early scope estimation is a common problem in software projects, leading to failures in meeting project requirements. Agile projects usually do not concentrate on a comprehensive requirements analysis and specification before the start of the project, making scope assessment difficult. This paper presents the methodology for facilitating a more accurate early estimation of project scope, based on requirements information gathered in various forms (requirements models and textual descriptions) during the requirements workshop. The requirements from different sources are compiled into one list and reconciled, since they are prepared by a number of participants in the requirements workshop using different notations (UML diagrams, SysML models, Story map) and may have differences in the vocabulary. Reconciliation encompasses the unification of vocabulary, as well as the identification and the removal of overlaps in requirements. The final list of requirements is used to estimate the scope of the project in story points. The estimate can be presented to the client and used as a basis for the project contract. A case study on the application of the proposed methodology is presented, using the animal shelter information system as a development project. It demonstrates that the methodology is viable and can facilitate the gathering of a more extensive set of requirements, thus ensuring a more detailed scope estimation.

AGILE ESTIMATIONS: EMPIRICAL STUDY USING WEIGHTED COMPLEXITY FACTORS

Software time and cost estimates are always challenging due to the quickly changing software process. Especially Agile project estimations are complex, and the story points differ from person to person. Due to the person-to-person variations, project managers have difficulty arriving at the project cost for development projects with different sizes or complexity of systems/software. This paper proposes a new estimation model named the WEighted COmplexity Factor Estimation (WECOFE) Model to reduce the complexities of Agile story point estimations. Weighted complexities factors identify Low, Moderate, and High Project Level Factors and Story Level Factors. For each level, multiple factors were defined. Derived Weighted complexity based on Project, Requirement, and Resource Level factors. Each story's point estimation is explained based on weighted complexity defined and assigned factors values. Conducted study on various industry projects, showing that the WECOFE approach is promising by observing the Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE). Also, performed a t-test for one sprint project with 51 stories, and paired sample means are equal for WECOFE with actual story points spent by the team.

Rethinking housing inequality and justice in a settler colonial city

This paper brings the housing studies literature into conversation with scholarship on settler colonialism to consider questions of housing justice in settler colonial societies. It begins from an understanding of Indigenous dispossession as not simply an historical context but an ongoing process in which housing is deeply implicated through its embeddedness in colonial land and property regimes. Furthermore, processes of exclusion, neoliberalisation, financialisation and gentrification have made housing central to the production of racial inequality in (and beyond) settler colonies. Liberal notions of inequality that dominate most housing policy and some scholarship are, therefore, inadequate; what is required is research and intervention oriented towards housing justice – the meaning of which is best understood through intellectual and practical engagement with movements struggling for it. In this spirit, the paper presents the story of a prolonged struggle in so-called Sydney, which suggests how housing initiatives and campaigns led by Indigenous people are iteratively reconstructing existing political and economic structures in ways that address commodification, dispossession, exclusion, and displacement. This story points to a need for housing justice scholarship to engage with concepts of reparations – an engagement towards which this paper, in its final section, makes some small but hopefully helpful steps.

『구비문학연구』 30년, 민담분석의 성과와 과제

This discussion, spanning the past 30 years since the establishment of the Korean Oral Literature Society in 1993, focuses on studies of folktales in the Journal of Korean Oral Literature and explores the achievements and issues in the analysis of folktales transferred orally, with the central question being, “What does it mean to analyze folktales in a truly folkloric manner?”&#x0D; Research on motifs in folktales can be broadly categorized into two types. The first aims to demonstrate that even when myths are transformed into folktaless, the attributes of mythic motifs remain intact and functional. The second focuses on openness, examining the profound polysemic meanings folktales generate based on the method of comparing motifs in various folktales to neighboring types. Studying how symbolic motifs are intricately connected to the story, and how they encapsulate and manifest meaning within folktales is a task that needs to be interpreted more profoundly within the realm of Korean oral literature.&#x0D; In the discussion of the achievements and challenges in analyzing the meaning of folktales through their story structures, this article examined the trajectory of research on the form of oral literature, which allows for ease of memory and reproduction while also permitting variations. This article also forecasts that the interpretive analysis of Korean folktales will become more systematic through structural analysis, such as the unit of story point that explores rich contextual interpretations of Korean folktales, the grammar of stories, the process of analysis of folktales centered on narrative issues, and expanded patterns of folktales.&#x0D; The chapter “Changes in Cognition and Emotion through Problem-Solving Methods in Folktales and the Creation of Contemporary Discourse” explains that important clues to solving the problems of modern life may be found in folktales regarding the essence of nature, the profound insights acquired by a remarkable person, or the wisdom of women. Through this, trends in the history of research on folktales that generate modern philosophical, social, and cultural discourse weremay also be examined. In studies of folktales with the theme of “conflicts in family relations,”’ the focus is on discussions that create alternative values by analyzing the “reverse side” to problems that arise when filial piety is the prevailing discourse, going beyond the dominant notions and common ideas. Literary therapy explores human narrative and seeks to identify the “problems” and “solutions” in human relationships and our lives that appear in folktales. Through literary therapy, research on folktales that can lead to emotional change will expand in the future.&#x0D; Finally, in modern society, with the advent of the AI era, it will be necessary to go beyond forexamining folklore materials based solely on the index of motifs. Instead, we should construct a comprehensive database that encompasses compound and neighboring types by utilizing the characteristics of oral literature, such as motifs and paragraphs, search for meanings, stacking, and variations. To achieve this, ongoing research on the categorization and systems of classification of folktales is essential.

Evaluating Service Computing Framework with Scrum through a Case Study

The Service Computing Framework with Scrum (SCFS) is a framework that combines Services Computing Systems Engineering (SCSE) with the Scrum Framework. This framework was created to produce a system with a good service concept by following the SCSE workflow in an agile manner through the application of Scrum principles. This paper will provide an overview of how to evaluate the SCFS framework using SOA and Agile principles through a case study. The research methodology used in this paper is a Design Science Research Methodology (DSRM). This research results in project evaluations using SCFS with good Service Oriented Architecture (SOA) parameter results. The SCFS framework can also be used in Agile development environments with fairly good parameters of velocity, story points, sprint burn down, release burn up, value delivered, and job satisfaction. In case studies conducted, this framework can help accelerate the release of service systems. The first release on the test only took 34 working days, 18 working days to get the second release, and 21 working days to get the third release. If done as a whole in one cycle, the service system will take approximately 73 working days to be used.

Data-driven agile software cost estimation models for DHS and DoD

Problem:Since the Manifesto for Agile Software Development, the cost analysis community has struggled to find the most appropriate size measures for developing accurate agile software development cost estimates at an early phase to establish baseline budgets and schedules, and for the selection of competitive bidders. At this time, common agile sizing measures such as Story Points are not practical since these are reported months after contract award in the Department of Homeland Security (DHS) and Department of Defense (DoD). The problem is compounded with the lack of data to build estimation models for informed decisions. Aims:The primary objective is to investigate how well two new size measures (Functional Story and Issues) accurately relate to total effort, and how these compare and rank against four popular software size measures (Story, Story Point, Unadjusted Function Points, and Simple Function Points). The second objective is to rank the six sizing measures based on how well each estimate software development effort at an early phase or after contract award. Method:The experimental framework relied on an analysis of variance and goodness-of-fit tests to examine and compare the accuracy of effort estimation models using six competing size measures. The analysis is based on data from 17 agile projects implemented between 2014 to 2021. Results:Our two new size measures (Functional Story, Issues) proved to be good predicters of total software development effort. Functional Story is better at predicting total effort at early phase than Function Points and Story. Functional Story and Function Points are better at predicting total effort after contract award than Story Point and Issues. Conclusion:The DHS and DoD cost community can choose one or more of these estimation models to evaluate agile software development cost proposals or track agile developer’s progress after contract award.

Agile Effort Estimation: Have We Solved the Problem Yet? Insights From a Replication Study

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.

Effort and Cost Estimation Using Decision Tree Techniques and Story Points in Agile Software Development

Early effort estimation is important for efficiently planning the use of resources in an Information Technology (IT) project. However, limited research has been conducted on the topic of effort estimation in agile software development using artificial intelligence. This research project contributes to strengthening the use of hybrid models composed of algorithmic models and learning oriented techniques as a project-level effort estimation method in agile frameworks. Effort estimation in agile methods such as Scrum uses a story point approach that measures, using an arithmetic scale, the effort required to complete a release of the system. This project relied on labeled historical data to estimate the completion time measured in days and the total cost of a project set in Pakistani rupees (PKR). using a decision tree, random forest and AdaBoost to improve the accuracy of predictions. Models were trained using 10-fold cross-validation and the relative error was used as a comparison with literature results. The bootstrap aggregation (bagging) ensemble made of the three techniques provides the highest accuracy, and project classification also improves the estimates.

GPT2SP: A Transformer-Based Agile Story Point Estimation Approach

Story point estimation is a task to estimate the overall effort required to fully implement a product backlog item. Various estimation approaches (e.g., Planning Poker, Analogy, and expert judgment) are widely-used, yet they are still inaccurate and may be subjective, leading to ineffective sprint planning. Recent work proposed Deep-SE, a deep learning-based Agile story point estimation approach, yet it is still inaccurate, not transferable to other projects, and not interpretable. In this paper, we propose GPT2SP, a Transformer-based Agile Story Point Estimation approach. Our GPT2SP employs a GPT-2 pre-trained language model with a GPT-2 Transformer-based architecture, allowing our GPT2SP models to better capture the relationship among words while considering the context surrounding a given word and its position in the sequence and be transferable to other projects, while being interpretable. Through an extensive evaluation on 23,313 issues that span across 16 open-source software projects with 10 existing baseline approaches for within- and cross-project scenarios, our results show that our GPT2SP approach achieves a median MAE of 1.16, which is (1) 34%-57% more accurate than existing baseline approaches for within-project estimations; (2) 39%-49% more accurate than existing baseline approaches for cross-project estimations. The ablation study also shows that the GPT-2 architecture used in our approach substantially improves Deep-SE by 6%-47%, highlighting the significant advancement of the AI for Agile story point estimation. Finally, we develop a proof-of-concept tool to help practitioners better understand the most important words that contributed to the story point estimation of the given issue with the best supporting examples from past estimates. Our survey study with 16 Agile practitioners shows that the story point estimation task is perceived as an extremely challenging task. In addition, our AI-based story point estimation with explanations is perceived as more useful and trustworthy than without explanations, highlighting the practical need of our Explainable AI-based story point estimation approach.

Effort Estimation in Agile Software Development: A Systematic Map Study

Introduction − Making effort estimation as accurate and suitable for software development projects becomes a fundamental stage to favor its success, which is a difficult task, since the application of these techniques in constant changing agile development projects raises the need to evaluate different methods frequently. Objectives− The objective of this study is to provide a state of the art on techniques of effort estimation in agile software development (ASD), performance evaluation and the drawbacks that arise in its application. Method− A systematic mapping was developed involving the creation of research questions to provide a layout of this study, analysis of related words for the implementation of a search query to obtain related studies, application of exclusion, inclusion, and quality criteria to filter nonrelated studies and finally the organization and extraction of the necessary information from each study. Results− 25 studies were selected; the main findings are: the most applied estimation techniques in agile contexts are: Estimation of Story Points (SP) followed by Planning Poker (PP) and Expert Judgment (EJ). The most frequent solutions supported in computational techniques such as: Naive Bayes, Regression Algorithms and Hybrid System; also, the performance evaluation measures Mean Magnitude of Relative Error (MMRE), Prediction Assessment (PRED) and Mean Absolute Error (MAE) have been found to be the most commonly used. Additionally, parameters such as feasibility, experience, and the delivery of expert knowledge, as well as the constant particularity and lack of data in the process of creating models to be applied to a limited number of environments are the challenges that arise the most when estimating software in agile software development (ASD) Conclusions− It has been found there is an increase in the number of articles that address effort estimation in agile development, however, it becomes evident the need to improve the accuracy of the estimation by using estimation techniques supported in machine learning that have been shown to facilitate and improve the performance of this.

Story Point Estimation Using Issue Reports With Deep Attention Neural Network

Background: Estimating the effort required for software engineering tasks is incredibly tricky, but it is critical for project planning. Issue reports are frequently used in the agile community to describe tasks, and story points are used to estimate task effort. Aim: This paper proposes a machine learning regression model for estimating the number of story points needed to solve a task. The system can be trained from raw input data to predict outcomes without the need for manual feature engineering. Method: Hierarchical attention networks are used in the proposed model. It has two levels of attention mechanisms implemented at word and sentence levels. The model gradually constructs a document vector by grouping significant words into sentence vectors and then merging significant sentence vectors to create document vectors. Then, the document vectors are fed into a shallow neural network to predict the story point. Results: The experiments show that the proposed approach outperforms the state-of-the-art technique Deep-S which uses Recurrent Highway Networks. The proposed model has improved Mean Absolute Error (MAE) by an average of 16.6% and has improved Median Absolute Error (MdAE) by an average of 53%. Conclusion: An empirical evaluation shows that the proposed approach outperforms the previous work.

Prediction of Software Effort by Using Non-Linear Power Regression for Heterogeneous Projects Based on Use case Points and Lines of code

Software cost estimation is necessary for all software organizations for contract negotiation. Numerous researchers have worked over the last four decades to provide estimation models, which can estimate efforts as accurately as they could, but the continuous change in the development models and use of new programming languages requires the development of new techniques for effort estimation. The size of the project must be estimated to determine the effort involved, which is rely on the programming languages and techniques employed for its development. Use case points, object points, lines of code, and story points are a few of the size matrices that are used for estimation using algorithmic models. There are several estimating techniques for the estimation of algorithmic models for various development approaches, but none of them offers a unified model for effort estimation for procedural and object-oriented projects. This work identifies common parameters for object-oriented and procedural-oriented programming languages. After the identification of common parameters, a combined model is developed for the estimation of effort for object-oriented and procedural-oriented programming language projects. The model's input is the size, which is measured in use case points and lines of source code. Power regression analysis of non-linear regression is applied to design the proposed model is designed using. The identified common parameters are multiplied with the mathematical equation achieved by power regression analysis to estimate the development effort for heterogeneous projects. The proposed work is assessed with the mean magnitude of relative error (MMRE). The error calculated with the proposed model is -0.16. The values of MMRE are further compared with the existing techniques such as linear regression, case-based reasoning, ensemble model, radial basis function neural network, and fuzzy analytic hierarchy process, and it was perceptible from the result that the proposed model delivers minimum error. The proposed work delivers the highest accuracy for software project estimation with the least amount of error.

Interrogating the Myths of Education: Dorothy Bryant'sElla Price's JournalandMiss Giardino

Interrogating the Myths of Education: Dorothy Bryant's<i>Ella Price's Journal</i>and<i>Miss Giardino</i>

Adapting Scrum for Software Capstone Courses

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student’s workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student’s learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor’s evaluations, student’s feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student’s satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.