Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

Customer Discovery and Customer Validation in Lean Software Startups

The traditional business models and the traditionally successful development methods that have been distinctive to the industrial era, do not satisfy the needs of modern IT companies. Due to the rapid nature of IT markets, the uncertainty of new innovations‟ success and the overwhelming competition with established companies, startups need to make quick decisions and eliminate wasted resources more effectively than ever before. There is a need for an empirical basis on which to build business models, as well as evaluate the presumptions regarding value and profit. Less than ten years ago, the Lean software development principles and practices became widely well-known in the academic circles. Those practices help startup entrepreneurs to validate their learning, test their assumptions and be more and more dynamical and flexible. What is special about today‟s software startups is that they are increasingly individual. There are quantitative research studies available regarding the details of Lean startups. Broad research with hundreds of companies presented in a few charts is informative, but a detailed study of fewer examples gives an insight to the way software entrepreneurs see Lean startup philosophy and how they describe it in their own words. This thesis focuses on Lean software startups‟ early phases, namely Customer Discovery (discovering a valuable solution to a real problem) and Customer Validation (being in a good market with a product which satisfies that market). The thesis first offers a sufficiently compact insight into the Lean software startup concept to a reader who is not previously familiar with the term. The Lean startup philosophy is then put into a real-life test, based on interviews with four Finnish Lean software startup entrepreneurs. The interviews reveal 1) whether the Lean startup philosophy is actually valuable for them, 2) how can the theory be practically implemented in real life and 3) does theoretical Lean startup knowledge compensate a lack of entrepreneurship experience. A reader gets familiar with the key elements and tools of Lean startups, as well as their mutual connections. The thesis explains why Lean startups waste less time and money than many other startups. The thesis, especially its research sections, aims at providing data and analysis simultaneously.

Cohesion Metrics for Improving Software Quality

Abstract Software product metrics aim at measuring the quality of software. Modu- larity is an essential factor in software quality. In this work, metrics related to modularity and especially cohesion of the modules, are considered. The existing metrics are evaluated, and several new alternatives are proposed. The idea of cohesion of modules is that a module or a class should consist of related parts. The closely related principle of coupling says that the relationships between modules should be minimized. First, internal cohesion metrics are considered. The relations that are internal to classes are shown to be useless for quality measurement. Second, we consider external relationships for cohesion. A detailed analysis using design patterns and refactorings confirms that external cohesion is a better quality indicator than internal. Third, motivated by the successes (and problems) of external cohesion metrics, another kind of metric is proposed that represents the quality of modularity of software. This metric can be applied to refactorings related to classes, resulting in a refactoring suggestion system. To describe the metrics formally, a notation for programs is developed. Because of the recursive nature of programming languages, the properties of programs are most compactly represented using grammars and formal lan- guages. Also the tools that were used for metrics calculation are described.

User Experience Patterns for/of Innovation by Design: A Survey on Design Practices in the Software Development and Innovation Lifecycle

The User Experience (UX) designers are undoubtedly aware of how many UX design methods currently exist and that sometimes it becomes a problem to choose an appropriate one. What are all of methods that designers have in their “arsenal”? When can they use them? This thesis presents the research on the design methods in the contemporary context of User Experience (UX) and Innovations by using a survey approach. The study is limited to cover the domain of consumer mobile services development and provider companies around the world. The study follows 2 clear objectives: (1) to understand what design methods are currently used in that context and to what extent they are used (2) to identify at what stage according to the UX design thinking process for creating innovations they are placed. The study contributes to the research in the field of UX design and Innovations and extends the knowledge in that field together with communities’ (UXPA, SIGCHI, SIGSOFT) members’ cooperation. The research is vital due to lack of information on design practices and their application in the chosen context.

Anxiety and learning / response behaviour in the context of intellectual problem solving by young children

Twenty-eight grade four students were ca.tegorized as either high or low anxious subjects as per Gillis' Child Anxiety Scale (a self-report general measure). In determining impulsivity in their response tendencies, via Kagan's Ma.tching Familiar Figures Test, a significant difference between the two groups was not found to exist. Training procedures (verbal labelling plus rehearsal strategies) were introduced in modification of their learning behaviour on a visual sequential memory task. Significantly more reflective memory recall behaviour was noted by both groups as a result. Furthermore, transfer of the reflective quality of this learning strategy produced significantly less impulsive response behaviour for high and low anxious subjects with respect to response latency and for low anxious subjects with respect to response accuracy.

The impact of integrated programming on student attitude and achievement in grade 9 academic mathematics and science

This research studioo the effect of integrated instruction in mathematics and~ science on student achievement in and attitude towards both mathematics and science. A group of grade 9 academic students received instruction in both science and mathematics in an integrated program specifically developed for the purposes of the research. This group was compared to a control group that had received science and mathematics instruction in a traditional, nonintegrated program. The findings showed that in all measures of attitude, there was no significant difference between the students who participated in the integrated science and mathematics program and those who participated in a traditional science and mathematics program. The findings also revealed that integration did improve achievement on some of the measures used. The performance on mathematics open-ended problem-solving tasks improved after participation in the integrated program, suggesting that the integrated students were better able to apply their understanding of mathematics in a real-life context. The performance on the final science exam was also improved for the integrated group. Improvement was not noted on the other measures, which included EQAO scores and laboratory practical tasks. These results raise the issue of the suitability of the instruments used to gauge both achievement and attitude. The accuracy and suitability of traditional measures of achievement are considered. It is argued that they should not necessarily be used as the measure of the value of integrated instruction in a science and mathematics classroom.

Genetic programming for the RoboCup Rescue Simulation System

The Robocup Rescue Simulation System (RCRSS) is a dynamic system of multi-agent interaction, simulating a large-scale urban disaster scenario. Teams of rescue agents are charged with the tasks of minimizing civilian casualties and infrastructure damage while competing against limitations on time, communication, and awareness. This thesis provides the first known attempt of applying Genetic Programming (GP) to the development of behaviours necessary to perform well in the RCRSS. Specifically, this thesis studies the suitability of GP to evolve the operational behaviours required of each type of rescue agent in the RCRSS. The system developed is evaluated in terms of the consistency with which expected solutions are the target of convergence as well as by comparison to previous competition results. The results indicate that GP is capable of converging to some forms of expected behaviour, but that additional evolution in strategizing behaviours must be performed in order to become competitive. An enhancement to the standard GP algorithm is proposed which is shown to simplify the initial search space allowing evolution to occur much quicker. In addition, two forms of population are employed and compared in terms of their apparent effects on the evolution of control structures for intelligent rescue agents. The first is a single population in which each individual is comprised of three distinct trees for the respective control of three types of agents, the second is a set of three co-evolving subpopulations one for each type of agent. Multiple populations of cooperating individuals appear to achieve higher proficiencies in training, but testing on unseen instances raises the issue of overfitting.

Predictors of shy children's coping with a social conflict : mediators and moderators

Shy children are at risk for later maladjustment due to ineffective coping with social conflicts through reliance on avoidance, rather than approach-focused, coping. The purpose of the present study was to explore whether the relation between shyness and children's coping was mediated by attributions and moderated by personality selftheories and gender. Participants included a classroom-based sample of 175 children (93 boys), aged 9-13 years (M = 10.11 years, SD = 0.92). Children completed self-report measures assessing shyness, attributions, personality self-theories and coping strategies. Results showed that negative attribution biases partially mediated the negative relations between shyness and social support seeking, as well as problem-solving, and the positive association between shyness and externalizing. Moreover, self-theories moderated the relation between shyness and internalizing coping at the trend level, such that the positive relation was exacerbated among entity-oriented children to a greater degree than incrementally-oriented children. In terms of gender differences, shyness was related to lower use of social support and problem-solving among incrementally-oriented boys and entity-oriented girls. Thus, shy children's perceptions of social conflicts as the outcome of an enduring trait (e.g., social incompetence) may partially explain why they do not act assertively and aggress as a means of social coping. Furthermore, entity-oriented beliefs may exacerbate shy children's reliance on internalizing actions, such as crying. Although an incrementally-oriented stance may enhance shy girls' reliance on approach strategies, it does not appear to serve the same protective role for shy boys. Therefore, copingoriented interventions may need to focus on restructuring shy children's social cognitions and implementing gender-specific programming for their personality biases.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Inverse Illumination Design with Genetic Programming

Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.