Software Metrics Classification for Agile Scrum Process: A Literature Review

Kurnia, Reni; Ferdiana, Ridi; Wibirama, Sunu

doi:10.1109/isriti.2018.8864244

Cited by 12 publications

(31 citation statements)

References 15 publications

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“…Story points used as a unit of measurement in user stories to calculate effort estimation to assess the effort involved in an issue [12]. Typically, story points follow Fibonacci like a sequence; 0,1,1,2,3,5,8,13,21,34,55,89,144, ..…”

Section: Methodsmentioning

confidence: 99%

“…The term pre-processing used to convert to lean data from raw data. The preprocessing steps of data include cleaning the data, transforming the data, removal of missing fields and outliers, normalize the database [12]. The purpose of normalization is to normalize data in a single type [11].…”

Section: Data Pre-processingmentioning

confidence: 99%

“…The main assumption for logistic regression is that the events are independent. Logistic Regression has used when the number of events has two groups either X1 or X2 [12]. It used to solve such type of questions "Is age a predictor of cancer, allowing for the other covariates?…”

Section: Logistic Regressionmentioning

confidence: 99%

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Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

Verma

Popli

Kumar

et al. 2021

Communications in Computer and Information Science

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Healthcare diseases diagnoses projects often exceed schedules and budgets due to the complication and their large size. Project planning and estimation are exclusively challenging in large and globally distributed projects. Agile with machine learning techniques can overcome these challenges of healthcare projects largely. The purpose of this study is to estimate efforts in large-scale healthcare projects using user story point with machine learning technique. Will be using user stories to estimates the size of Healthcare projects. The templates of user stories: As a [user or destination], I [want to some goal] so that [the goal can be achieved]. According to Bill Wake a good user story must have INVEST acronym. To predict accuracy of machine learning algorithms such as Naïve Bayes, Decision Tree, Logistic Regression cancer patient dataset has used. Writing user stories for healthcare is an easy way to maintain the requirements for an agile project. Agile model in healthcare has used to maintain the effectiveness by adopting the frequently changing demand of the customers. This research paper work on cancer diseases and will provide accuracy, precision, recall using machine learning algorithms such as naïve Bayes, logistic regression, Decision Tree.

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Section: Methodsmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

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Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

Verma

Popli

Kumar

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

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“…Anderson in his book [18] describes a set of Agile metrics inspired by Toyota Production System [19] as a core of the Kanban Method. Literature studies performed in recent years by Kupiainen et al [20] and Kurnia et al [21] indicate that ASDT use different metrics in their work and measuring different aspects of Agile software development is becoming a standard practice.…”

Section: Related Workmentioning

confidence: 99%

The State of Agile Software Development Teams During the Covid-19 Pandemic

Marek

Wińska

Dąbrowski

2021

Lecture Notes in Business Information Processing

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The Covid-19 pandemic in 2020 forced Agile Software Development Teams (ASDT) to rapidly transition to remote work and adapt to new business circumstances. The focus of this research was to investigate the impact of the Covid-19 pandemic on ASDT work and what tools and metrics are used by ASDT. A global survey was performed with 120 answers from different software engineering teams. The results of the research indicate that the work of ASDT wasn't significantly impacted. Most of the ASDT had experience with working in a distributed or remote environment. Therefore, most of the ASDT were able to transitioned to full remote work. Results indicate the Covid-19 pandemic didn't have much impact on Product Backlog and Vision. Moreover, most ASDT didn't change their Definition of Done and release frequency, indicating that the pace and quality of work wasn't disturbed during the Covid-19 pandemic. The few ASDT that changed their work organization did it together with changes to Product Backlog and Vision. Results indicate that the prevalence of distributed teams and remote work among ASDT helped with the transition to fully remote work during the Covid-19 pandemic. Additionally, this article presents gathered data of popularity of different online cooperation tools and metrics used by ASDT.

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“…A Systematic Mapping Study on Dynamic Metrics and Software Quality (2012) [31] ID-5. Software Metrics Classification for Agile Scrum Process: A Literature Review (2018) [32] ID-6. Methods for estimating agile software projects: A Systematic literature review (2018) [33] ID-7.…”

Section: Selection Of Studiesmentioning

confidence: 99%

Measures related to social and human factors that influence productivity in software development teams

Machuca-Villegas

Gasca‐Hurtado

Muñoz

2021

IJISPM

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Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team's performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts.

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Software Metrics Classification for Agile Scrum Process: A Literature Review

Cited by 12 publications

References 15 publications

Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

The State of Agile Software Development Teams During the Covid-19 Pandemic

Measures related to social and human factors that influence productivity in software development teams

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