Assessing and improving measurability of process performance indicators based on quality of logs

2021 ◽  
pp. 101874
Author(s):  
Cinzia Cappiello ◽  
Marco Comuzzi ◽  
Pierluigi Plebani ◽  
Matheus Fim
Keyword(s):  
Performance Indicators ◽  
Process Performance

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.

Principals’ Policy Preferences concerning Accountability: Implications for Key Competencies, Performance Indicators, and Professional Development

1995 ◽  
Vol 5 (5) ◽  
pp. 448-481 ◽  
Author(s):  
R. J. S. Mac Macpherson ◽  
Margaret Taplin
Keyword(s):  
Professional Development ◽  
Performance Indicators ◽  
Policy Preferences ◽  
Development Programs ◽  
Stakeholder Groups ◽  
Key Competencies ◽  
Professional Development Programs ◽  
Accountability Practices ◽  
State Schools

In this paper, we examine the policy preferences of Tasmania's principals concerning accountability criteria and processes, compare their views to other stakeholder groups, and identify issues that warrant attention in principals’ professional development programs. We show that there are many criteria and processes related to the quality of learning, teaching, and leadership that are valued by all stakeholder groups, including principals. We conclude that Tasmanian state schools probably need to review and develop their accountability policies, and that the professional development will need to prepare leaders for specific forms of performance and generate key competencies if more educative forms of accountability practices are to be realised in practice.

scholarly journals Galileo L10 Satellites: Orbit, Clock and Signal-in-Space Performance Analysis

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1695
Author(s):  
Constantin-Octavian Andrei ◽  
Sonja Lahtinen ◽  
Markku Poutanen ◽  
Hannu Koivula ◽  
Jan Johansson
Keyword(s):  
Performance Indicators ◽  
Satellite System ◽  
Short Term ◽  
Orbital Inclination ◽  
Navigation Data ◽  
Periodic Signals ◽  
Global Navigation Satellite ◽  
Minor Correction ◽  
Very High

The tenth launch (L10) of the European Global Navigation Satellite System Galileo filled in all orbital slots in the constellation. The launch carried four Galileo satellites and took place in July 2018. The satellites were declared operational in February 2019. In this study, we report on the performance of the Galileo L10 satellites in terms of orbital inclination and repeat period parameters, broadcast satellite clocks and signal in space (SiS) performance indicators. We used all available broadcast navigation data from the IGS consolidated navigation files. These satellites have not been reported in the previous studies. First, the orbital inclination (56.7±0.15°) and repeat period (50680.7±0.22 s) for all four satellites are within the nominal values. The data analysis reveals also 13.5-, 27-, 177- and 354-days periodic signals. Second, the broadcast satellite clocks show different correction magnitude due to different trends in the bias component. One clock switch and several other minor correction jumps have occurred since the satellites were declared operational. Short-term discontinuities are within ±1 ps/s, whereas clock accuracy values are constantly below 0.20 m (root-mean-square—rms). Finally, the SiS performance has been very high in terms of availability and accuracy. Monthly SiS availability has been constantly above the target value of 87% and much higher in 2020 as compared to 2019. Monthly SiS accuracy has been below 0.20 m (95th percentile) and below 0.40 m (99th percentile). The performance figures depend on the content and quality of the consolidated navigation files as well as the precise reference products. Nevertheless, these levels of accuracy are well below the 7 m threshold (95th percentile) specified in the Galileo service definition document.

scholarly journals The potential of e-ticketing for public transport planning: the Piedmont region case study

2016 ◽  
Author(s):  
Maurizio Arnone
Keyword(s):  
Performance Indicators ◽  
Load Factor ◽  
User Mobility ◽  
Mobility Patterns ◽  
Trace Back ◽  
Travel Diary ◽  
Full Activation ◽  
Piedmont Region

In the Piedmont region (Italy) the electronic ticketing system called BIP, is currently active across much of its territory, and thedata collected in the Province of Cuneo since the full activation of the system (2014) provide today a sound source ofinformation. Two different travel documents are available, travel passes and pay-per-use, with different validation rules: check-inonly for travel passes and check-in and check-out for pay-per-use. Data produced by this electronic ticketing system employingsmart cards allow to perform a detailed analysis of each user’s behaviour, and calculate time and space distributions of eachpassenger trip. In detail, data originating from smart card transactions allow to trace back the trip chains, establish journey originsand destinations, and produce a “travel diary” for each passenger. Based on this data, performance indicators (i.e. load factor) aswell as user mobility patterns and origin-destination matrices can be calculated in an automated and reliable way. This articlepresents a methodology for assessing the quality of the data collected when information about boarding and alighting stops isavailable from the (on board) validation system. It also presents an algorithm to assign a destination for each trip where only theboarding information is available. In the case study of the Province of Cuneo, it was found that 91% of the pay-per-use journeydata are reliable and can be used for further analysis, whereas with the use of the proposed algorithm it was possible to estimatethe destinations for 82% of the travel pass trips.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1999

scholarly journals Methodology for the Formation of Assessment Values of Expert Performance Indicators for Perspective Automated Digital Telecommunication Systems

2021 ◽  
Vol 27 (3) ◽  
pp. 368-379
Author(s):  
D. M. Nenadovich ◽  
I. V. Markin
Keyword(s):  
Performance Indicators ◽  
Quantitative Assessment ◽  
Telecommunication Networks ◽  
Expert Performance ◽  
Telecommunication System ◽  
Performance Quality ◽  
Telecommunication Systems

One of the main goals of modernization of telecommunication networks is to increase its performance. For a quantitative assessment of the success of improving the system, methods were developed for assessing the expert indicators of the performance quality of the designed digital telecommunication system, synthesized on the basis of previously developed decomposition and reduction algorithms. The main expert indicators of the performance quality of the designed system are considered, the dependences for calculating these indicators are presented.

scholarly journals Designing key performance indicators for higher education programs in engineering and technology

2017 ◽  
Vol 1 (Q3) ◽  
pp. 44-50
Author(s):  
Lam Ngoc Quynh Le ◽  
Dung The Vu ◽  
Hien Ngoc Do ◽  
Thoai Tuong Lam ◽  
Trang Thi Thuy Vuu ◽  
...  
Keyword(s):  
Higher Education ◽  
Performance Indicators ◽  
Action Plan ◽  
Key Performance Indicators ◽  
Global Integration ◽  
Education Programs ◽  
Quality Of Higher Education ◽  
Statistical Measures ◽  
Engineering And Technology

Higher education plays a crucial role in the economic and social development. In order to fulfill its role, mission, and vision, as well as enhance competitive advantages in the global integration context, universities have to put quality as the top priority. Recently, many universities nationwide have been developing quality assurance system, conducting self assessment, and pursuing accreditation in both program and institutional levels. This process aims at developing suitable action plan for quality improvement based on determined strengths and weaknesses. However, this process requires a huge effort to collect and analyze data. In order to enhance the effectiveness and efficiency, the key performance indicators (KPIs) are proposed to evaluate the quality of higher education programs in many aspects. Those KPIs can be considered as a set of statistical measures of how higher education programs are performing. This paper, therefore, provides some general definitions and proposes some KPIs in order to measure the quality of higher education programs in engineering and technology. However, findings of this study can be used as references for other programs.

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