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Software development is constantly growing in complexity and several newtools have been created with the aim to manage this. However, even with thisever evolving range of tools and methodology, organizations often struggle withhow to implement a new development-process, especially when implementingagile methods. The most common reason for this is because teams implementagile tools in an ad-hoc manner, without fully considering the effects this cancause. This leads to teams trying to correct their choice of methodologysomewhere during the post-planning phase, which can be devastating for aproject as it adds further complexity to the project by introducing new problemsduring the transition process. Moreover, with an existing range of tools aimedat managing this process transition, none of them have been thoroughlyevaluated, which in turn forms the problem that this thesis is centred around.This thesis explores a method transition scenario and evaluates a SoftwareProcess Improvement method oriented around the problems that theimprovement process is aiming to solve. The goal with this is to establish ifproblem oriented Software Process Improvement is viable as well as to providefurther data for the extensive research that is being done in this field. We wishto prove that the overall productivity of a software development team can beincreased even during a project by carefully managing the transition to newmethods using a problem driven approach.The research method used is of qualitative and inductive character. Data iscollected by performing a case study, via action research, and literature studies.The case study consists of iteratively managing a transition over to newmethods, at an organization in the middle of a project, using a problem drivenapproach to Software Process Improvement. Three iterations of methodimprovement are applied on the project and each iteration acts as an evaluationon how well Problem Driven Software Process Improvement works.By using the evaluation model created for this degree project, the researchershave found that problem driven Software Process Improvement is an effectivetool for managing and improving the processes of a development team.Productivity has increased with focus on tasks with highest priority beingfinished first. Transparency has increased with both development team andcompany having a clearer idea of work in progress and what is planned.Communication has grown with developers talking more freely about userstories and tasks during planning and stand-up meetings. The researchersacknowledge that the results of the study are of a limited scope and alsorecognize that further evaluation in form of more iterations are needed for acomplete evaluation.

Infrastructure systems are crucial facilities. They supply the necessary transportation, water and energy utilities for the public. However, while aging, these systems gradually deteriorate in time and approach the end of their lifespans. As a result, they require periodic maintenance and repair in order to function and be reliable throughout their lifetimes. Bridge infrastructure is an essential part of the transportation infrastructure. Bridge management systems (BMSs), used to monitor the condition and safety of the bridges in a bridge infrastructure, have evolved considerably in the past decades. The aim of BMSs is to use the resources in an optimal manner keeping the bridges out of risk of failure. The BMSs use the lifetime performance curves to predict the future condition of the bridge elements or bridges. The most widely implemented condition-based performance prediction and maintenance optimization model is the Markov Decision Process-based models (MDP). The importance of the Markov Decision Process-based model is that it defines the time-variant deterioration using the Markov Transition Probability Matrix and performs the lifetime cost optimization by finding the optimum maintenance policy. In this study, the Markov decision process-based model is examined and a computer program to find the optimal policy with discounted life-cycle cost is developed. The other performance prediction model investigated in this study is a probabilistic Bi-linear model which takes into account the uncertainties for the deterioration process and the application of maintenance actions by the use of random variables. As part of the study, in order to further analyze and develop the Bi-linear model, a Latin Hypercube Sampling-based (LHS) simulation program is also developed and integrated into the main computational algorithm which can produce condition, safety, and life-cycle cost profiles for bridge members with and without maintenance actions. Furthermore, a polynomial-based condition prediction is also examined as an alternative performance prediction model. This model is obtained from condition rating data by applying regression analysis. Regression-based performance curves are regenerated using the Latin Hypercube sampling method. Finally, the results from the Markov chain-based performance prediction are compared with Simulation-based Bi-linear prediction and the derivation of the transition probability matrix from simulated regression based condition profile is introduced as a newly developed approach. It has been observed that the results obtained from the Markov chain-based average condition rating profiles match well with those obtained from Simulation-based mean condition rating profiles. The result suggests that the Simulation-based condition prediction model may be considered as a potential model in future BMSs.