Computable metrics of personal performance in software development teams

2019 ◽  
Author(s):  
Andriy Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Product Quality ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Project Manager ◽  
Development Life Cycle ◽  
Software Product ◽  
Software Development Life Cycle

The present article describes setup, configuration and usage of the key performance indicators (KPIs) of members of project teams involved into the software development life cycle. Key performance indicators are described for the full software development life cycle and imply the deep integration with both task tracking systems and project code management systems, as well as a software product quality testing system. To illustrate, we used the extremely popular products - Atlassian Jira (tracking development tasks and bugs tracking system) and git (code management system). The calculation of key performance indicators is given for a team of three developers, two testing engineers responsible for product quality, one designer, one system administrator, one product manager (responsible for setting business requirements) and one project manager. For the key members of the team, it is suggested to use one integral key performance indicator per the role / team member, which reflects the quality of the fulfillment of the corresponding role of the tasks. The model of performance indicators is inverse positive - the initial value of each of the indicators is zero and increases in the case of certain deviations from the standard performance of official duties inherent in a particular role. The calculation of the proposed key performance indicators can be fully automated (in particular, using Atlassian Jira and Atlassian Bitbucket (git) or any other systems, like Redmine, GitLab or TestLink), which eliminates the human factor and, after the automation, does not require any additional effort to calculate. Using such a tool as the key performance indicators allows project managers to completely eliminate bias, reduce the emotional component and provide objective data for the project manager. The described key performance indicators can be used to reduce the time required to resolve conflicts in the team, increase productivity and improve the quality of the software product.

scholarly journals DevOps phases across Software Development Lifecycle

2021 ◽  
Author(s):  
Mayank Gokarna
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Implementation Strategy ◽  
Quality Of Services ◽  
Comprehensive Collection ◽  
Development Lifecycle ◽  
Development Life Cycle ◽  
Software Development Lifecycle ◽  
Software Development Life Cycle

DevOps is the combination of cultural mindset, practices, and tools that increases a team's ability to release applications and services at high velocity. The development and operations teams always have a conflict around the scope of responsibility. With these differences the quality and speed of delivery across software Development Life Cycle is negatively impacted. DevOps is about removing the barriers between two traditionally delimited teams, development and operations. With DevOps, these two teams work together to optimize both the productivity of developers and the reliability of operations. They strive to communicate frequently, increase efficiencies, and improve the quality of services they provide. They take full ownership for their services, often beyond where their stated roles or titles have traditionally been scoped. Transitioning to DevOps requires a change in culture and mindset first. It is quite difficult to persuade a whole company to change its culture at once. This paper aims to bring different phases of software development lifecycle into DevOps implementation strategy and presents a comprehensive collection of leading tools used across Software Development life Cycle to automate and integrate different stages of software delivery. This paper also highlights on DevOps practices which span across different phases of the Software Development Lifecycle and how those can be implemented with different tools available.

scholarly journals Architecture Evaluation of Mobile Application: Whatsapp

2019 ◽  
Vol 8 (10) ◽  
pp. 1253-1256
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Mobile Application ◽  
Life Quality ◽  
Stage Of Development ◽  
Development Life Cycle ◽  
Architecture Evaluation ◽  
Software Development Life Cycle ◽  
The One

Software become an unavoidable in every once life. Quality of the software is an import aspect in the software development life cycle. Quality for a software is represented in terms of functional and non-functional requirement. Software architecture is used to represent the using set of components and is connectivity as a relationship between these components. To assure the development process meet the requirement given by the user, the Software Evaluation is used. Early detection of error protect the software development producing the defect software. ATAM is the one of the method used to detect the risk, non-risk, scenarios and tradeoff in the earlier stage of development life cycle. Here in this paper security scenarios for mobile application has been elicited and compared with the scenarios extracted from the whatsapp application. Comparison shows few scenarios need to added with existing scenarios in order to improve / ensure full security for the metadata.

scholarly journals Perancangan Sistem Pengukuran Kompetensi Sistem Analis Berdasarkan Kemampuan Teknis dan Portofolio Berbasis Komputer (Computer Based Test)

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Ade Sugiandi
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Project Manager ◽  
Development Life Cycle ◽  
Computer Based ◽  
Software Development Life Cycle

Software Development Life Cycle (SDLC) merupakan kerangka kerja yang terstruktur berisi tahapan proses yang berurutan dimana sistem informasi dikembangkan. Setiap tahapan dari SDLC mempunyai penanggung jawab salah satunya adalah sistem analis yang memiliki tanggung jawab pada tahapan analisis dan desain sistem. Perananan sistem analis dalam pembangunan sistem sangat vital, untuk itu sistem analis harus memiliki kompetensi yang baik supaya bisa memastikan projek pembangunan sistem berjalan dengan baik. Pengukuran kompetensi selama ini tidak terstruktur dengan baik dan biasanya dilakukan hanya sekali pada saat ada kebutuhan. Kompetensi akan berkembang seiring dengan keterlibatan sistem analis didalam projek pembangunan sistem. Tahapan penelitian yang dilakukan salah satunya adalah survey terhadap responden yang berprofesi sebagai Sistem Analis dan Project Manager tujuan dari survey tersebut adalah untuk meminta tanggapan terhadap rancangan sistem pengukuran kompetensi yang akan dibuat dalam penelitian ini. Berdasarkan tanggapan survey mengenai usulan rancangan sistem yang disampaikan oleh responden bisa disimpulkan bahwa >90% responden setuju dengan rancangan yang disampaikan dapat mengukur kompetensi dari seorang sistem analis. Untuk itu rancangan sistem yang sudah dibuat bisa dijadikan acuan dalam pembangunan sistem pengukuran kompetensi sistem analis. Software Development Life Cycle (SDLC) merupakan kerangka kerja yang terstruktur berisi tahapan proses yang berurutan dimana sistem informasi dikembangkan. Setiap tahapan dari SDLC mempunyai penanggung jawab salah satunya adalah sistem analis yang memiliki tanggung jawab pada tahapan analisis dan desain sistem. Perananan sistem analis dalam pembangunan sistem sangat vital, untuk itu sistem analis harus memiliki kompetensi yang baik supaya bisa memastikan projek pembangunan sistem berjalan dengan baik. Pengukuran kompetensi selama ini tidak terstruktur dengan baik dan biasanya dilakukan hanya sekali pada saat ada kebutuhan. Kompetensi akan berkembang seiring dengan keterlibatan sistem analis didalam projek pembangunan sistem. Tahapan penelitian yang dilakukan salah satunya adalah survey terhadap responden yang berprofesi sebagai Sistem Analis dan Project Manager tujuan dari survey tersebut adalah untuk meminta tanggapan terhadap rancangan sistem pengukuran kompetensi yang akan dibuat dalam penelitian ini. Berdasarkan tanggapan survey mengenai usulan rancangan sistem yang disampaikan oleh responden bisa disimpulkan bahwa >90% responden setuju dengan rancangan yang disampaikan dapat mengukur kompetensi dari seorang sistem analis. Untuk itu rancangan sistem yang sudah dibuat bisa dijadikan acuan dalam pembangunan sistem pengukuran kompetensi sistem analis.

scholarly journals DevOps phases across Software Development Lifecycle

2021 ◽  
Author(s):  
Mayank Gokarna
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Implementation Strategy ◽  
Quality Of Services ◽  
Comprehensive Collection ◽  
Development Lifecycle ◽  
Development Life Cycle ◽  
Software Development Lifecycle ◽  
Software Development Life Cycle

DevOps is the combination of cultural mindset, practices, and tools that increases a team's ability to release applications and services at high velocity. The development and operations teams always have a conflict around the scope of responsibility. With these differences the quality and speed of delivery across software Development Life Cycle is negatively impacted. DevOps is about removing the barriers between two traditionally delimited teams, development and operations. With DevOps, these two teams work together to optimize both the productivity of developers and the reliability of operations. They strive to communicate frequently, increase efficiencies, and improve the quality of services they provide. They take full ownership for their services, often beyond where their stated roles or titles have traditionally been scoped. Transitioning to DevOps requires a change in culture and mindset first. It is quite difficult to persuade a whole company to change its culture at once. This paper aims to bring different phases of software development lifecycle into DevOps implementation strategy and presents a comprehensive collection of leading tools used across Software Development life Cycle to automate and integrate different stages of software delivery. This paper also highlights on DevOps practices which span across different phases of the Software Development Lifecycle and how those can be implemented with different tools available.

A Systematic Review and Catalog of Security Metric during the Secure Software Development Life Cycle

2020 ◽  
Vol 13 ◽  
Author(s):  
Sampada G.C ◽  
Tende Ivo Sake ◽  
Amrita
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Software Development ◽  
Software Security ◽  
Security Assessment ◽  
Security Metrics ◽  
Development Life Cycle ◽  
Development Processes ◽  
Secure Software Development ◽  
Software Development Life Cycle

Background: With the advancement in the field of software development, software poses threats and risks to customers’ data and privacy. Most of these threats are persistent because security is mostly considered as a feature or a non-functional requirement, not taken into account during the software development life cycle (SDLC). Introduction: In order to evaluate the security performance of a software system, it is necessary to integrate the security metrics during the SDLC. The appropriate security metrics adopted for each phase of SDLC aids in defining the security goals and objectives of the software as well as quantify the security in the software. Methods: This paper presents systematic review and catalog of security metrics that can be adopted during the distinguishable phases of SDLC, security metrics for vulnerability and risk assessment reported in the literature for secure development of software. The practices of these metrics enable software security experts to improve the security characteristics of the software being developed. The critical analysis of security metrics of each phase and their comparison are also discussed. Results: Security metrics obtained during the development processes help to improve the confidentiality, integrity, and availability of software. Hence, it is imperative to consider security during the development of the software, which can be done with the use of software security metrics. Conclusion: This paper reviews the various security metrics that are meditated in the copious phases during the progression of the SDLC in order to provide researchers and practitioners with substantial knowledge for adaptation and further security assessment.

Software Traceability – A Key to Improve Software Evolution

2016 ◽  
Vol 685 ◽  
pp. 881-885
Author(s):  
Alexey Ponomarev ◽  
Hitesh S. Nalamwar
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Software Evolution ◽  
Software Traceability ◽  
Development Life Cycle ◽  
Methods And Techniques ◽  
Software Development Life Cycle

Software traceability is an important part in software development that is getting more and more attention nowadays from organizations and researchers. The paper outlines the importance, different methods and techniques of software traceability. It also explains the need of automating traceability, problems and drawbacks of existing traceability tools, the ongoing challenges facing implementation of traceability in software development life cycle, and finally the paper discusses whether software traceability should be mandated as a key to improve software evolution

Project Implementation Decision Using Software Development Life Cycle Models: A Comparative Approach

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
M.A. Adeagbo ◽  
J.E.T. Akinsola ◽  
A.A. Awoseyi ◽  
F. Kasali
Keyword(s):  
Life Cycle ◽  
Comparative Analysis ◽  
Software Development ◽  
Cost Estimation ◽  
Comparative Approach ◽  
Project Implementation ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Waterfall Model ◽  
Spiral Model

Selection of a suitable Software Development Life Cycle (SDLC) model for project implementation is somewhat confusing as there are a lot of SDLC models with similar strengths and weaknesses. Also, the solutions proffered among the researchers so far have been the  qualitative comparative analysis of SDLC models. Hence, this paper proposes a comparative analysis of SDLC models using quantitative approach in relation to strengths and weaknesses of SDLC models. The study adapted comparative analysis and Software Development Life Cycle (SDLC) models features’ classification using ten characteristics such as project complexity, project size, project duration, project with risk, implementation/initial cost, error discovery, associated cost, risk analysis, maintenance and cost estimation. A quantitative measure that employs online survey using experts in software design and engineering, project management and system analysis was carried out for the evaluation of SDLC models. Purposeful Stratified Random Sampling (SRS) technique was used to gather the data for analysis using XLSTAT after pre-processing, taking into consideration both benefit and cost criteria. The overall performance evaluation showed that Spiral-Model is the best followed by V-Model and lastly Waterfall Model with comparative values of 38.63%, 35.76% and 25.61% respectively. As regards cost estimation, Waterfall Model is the most efficient with value of 41%, then V-Model with 31% and lastly Spiral Model with 28%. V-Model has great error recovery capability with value of 45% which is closely followed by Spiral Model with 37% and lastly Waterfall Model with 18%. The study revealed that, a model with efficient risk assurance does not guarantee efficient cost management. In the future work, more characteristics regarding SDLC models shall be considered.

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