ING Bank, a large Netherlands-based internationally operating bank, implemented a fully automated continuous delivery pipe-line for its software engineering activities in more than 300 teams, that perform more than 2500 deployments to production each month on more than 750 different applications. Our objective is to examine how strong metrics for agile (Scrum) DevOps teams can be set in an iterative fashion. We perform an exploratory case study that focuses on the classification based on predictive power of software metrics, in which we analyze log data derived from two initial sources within this pipeline. We analyzed a subset of 16 metrics from 59 squads. We identified two lagging metrics and assessed four leading metrics to be strong.

/pdf/strong-agile-metrics-mining-log-data-to-determine-predictive-14s4u2soo4.pdf

Strong agile metrics: mining log data to determine predictive power of software metrics for continuous delivery teams

During the last decades several trends have been recognized. An important one is the success of agile development methods. Another one is the increased importance of modeling. Specification languages like UML, BPMN or S-BPM gained a lot of attention and are applied in most projects currently. Additionally, a shifting focus from Technology-Driven Design to Human-Centered Design can be recognized. The paper discusses the existing idea of Continuous Software Engineering that extends traditional software engineering methods by business-administration approaches. It provides the idea of including maintenance into the life-cycle development. Business strategies and software development have to be integrated and have to be supported as a continuous process.Based on the analysis of the literature an even broader view of Continuous Software Engineering is provided. It includes now Business-Process Modeling and Human-Centered Design.

Continuous Software Engineering with Special Emphasis on Continuous Business-Process Modeling and Human-Centered Design

This paper proposes an approach based on mathematical modeling to provide decision making support for the Next Release Problem (NRP) arising in software development and maintenance companies that use SCRUM to manage teams and new releases of its software products. This approach aims to support the decisions from Product Owners and Teams about which requirements should be implemented for the next software release. Therefore it is proposed to use mixed integer programming in three different objective functions: (i) maximization of the customer satisfaction (considering their business value), (ii) minimization of software development time and (iii) minimization of the costs to produce the requirements. We consider constraints such as team availability, dependencies among requirements, the team's performance among others. The modeling was performed with the help of the AIMMS tool and some case studies were performed in three software development and maintenance companies using real data. The results show that it was possible to adapt the NRP for this kind of companies, achieving a better customer's satisfaction and business results using our approach.

Mixed Integer Programming helping Requirements Allocation for the NRP in SCRUM Teams

Agile software engineers’ affective states, their performance and software quality: A systematic mapping review

In Scrum, we measure performance using velocity. However, the velocity of one team cannot be compared to the velocity of another, since it is a relative measure that is only of meaning to the team using it. So can we objectively measure the performance of teams? Measuring Value Added Time as a percentage of Total Time is a metric that is used in Lean Manufacturing to help get a better understanding of production processes and optimize those processes. This paper introduces an adaptation of this metric to the Agile environment. Giving teams an objective insight into their efficiency helps them optimize their efficiency and compare themselves to other teams. This adapted metric is called Process Efficiency and is comparable across teams, technologies, and domains of practice.

/pdf/process-efficiency-adapting-flow-to-the-agile-improvement-3dp0ji4r5h.pdf

Process Efficiency - Adapting Flow to the Agile Improvement Effort

Recent surveys show that 42% of Agile projects are successful. While this is three times better than traditional projects, 49% of Agile projects are late or over budget and 9% are total failures [1]. There is a better way to help Agile teams to implement Scrum. At the 2013 Scrum PLoP Conference held in Tisvildeleje, Denmark thought leaders in the Agile community reviewed a set of Scrum Patterns that together generate a high performing Scrum team. During this editorial process it became apparent that a combination of nine Patterns in conjunction with the Scrum framework could help teams achieve Hyper-Productivity, more than a 400% increase in velocity over a team's initial velocity.

/pdf/teams-that-finish-early-accelerate-faster-a-pattern-language-37p0tz37fo.pdf

Teams That Finish Early Accelerate Faster: A Pattern Language for High Performing Scrum Teams

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Teams That Finish Early Accelerate Faster: A Pattern Language for High Performing Scrum Teams