Agile Software Engineering (Undergraduate Topics in Computer Science)
Add to that the specializations in design, business analysis, and security, and a growing number of sub-specializations specific to industry domains such as automotive or healthcare software. Whereas the past decades of software engineering created innovators and generalists, the coming ones will be all about specialists with deep domain and functional expertise. Today, the Association of American Medical Colleges lists more than specializations and subspecialties. The growing specialization in how software is built will require increasingly specialized education programs and training, as well as increasing breadth of practices and tools.
Some domains—for example, those with well-defined data models— will be outsourced to AI sooner than others. In a growing number of data-driven domains, AI will become better at some aspects of software engineering than human programmers. However, even as AI and automation increase dramatically, the number of humans involved in software delivery will grow.
This will be due to the spread of software into every industry, the growing specialization of roles, and the shift of business processes to software.
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Whether the software is being created by programmers or AI, the number of supporting specializations and the demand for those specialists will continue to grow throughout the Deployment Period. As the adoption of AI grows, the way that some software is built will change. The human-communication-intensive aspects, such as designing and defining requirements or user experiences specific to business processes, are likely to still require growing numbers of people.
However, for domains suited to ongoing AI developments, some traditional programming work will shift to data modeling. Similarly, data science roles will continue to grow in data-intensive domains. Computation, processing, and storage prices will continue to drop; capabilities will continue to increase. Dramatic advances, such as quantum computation, could take hold. As that happens, new jobs and specializations will emerge around producing and analyzing the results of increasingly massive computational and data capabilities. This will then need to be fed back into the creative process of defining business strategies and producing new customer offerings.
If such a sixth wave is currently forming, we can imagine it to be as profound as this one. Will that new department be built around general or strong AI that is, artificial general intelligence? Or, will computation and AI become so pervasive as to become a core part of every department and business practice? During each of these surges, the bulk of the groundbreaking invention occurs during the Installation Period.
This is when the combination of a new technological system and entrepreneurship fuels invention and is exploited by new companies that become the technology giants of their age. In the last age, examples of those were the car companies that mastered mass production. During the Deployment Period, if what we witness is similar to the past four ages, over the coming two decades, the rest of the economy will slowly start to see similar productivity gains as software practices are adopted and mastered across industries and organizations. We can expect to shift to more incremental innovation and a slowing pace of new breakthroughs in our field.
The Perez model also predicts that candidates for the next technological wave start arising during the Deployment Period. But notably, the new wave will grow largely independently of the ongoing diffusion of this one. Given that software engineering is headed into a diffusion phase, opportunities will grow for deploying the ideas and innovations of the Installation Period into existing and new business models at a much greater scale than in the past 50 years.
The computational ideas formed in the past 50 years will become much more broadly adopted over the next 50 years, and in the process will be tailored to each segment of the economy. To support this, the principles of software engineering will need to spread much more broadly and deeply within businesses.
Software is already becoming a key competitive differentiator for business offerings ranging from financial services to natural resources. It has already the norm for large banks to have tens of thousands of IT staff and for oil companies to manage portfolios of hundreds of internal software applications.
With software becoming more ubiquitous in relation to the means of production and competitive differentiation, computational thinking will become key to many professional and business functions. As this happens, the nature of programming will evolve. In the past, programmers tended to be trained as generalists. The specialization goes beyond the code. Some Internet-scale software vendors split their software delivery staff evenly between development and operations, foreshadowing the increasing importance of infrastructure engineering.
As infrastructure continues to turn into code, specialized operations roles are emerging. Add to that the specializations in design, business analysis, and security, and a growing number of sub-specializations specific to industry domains such as automotive or healthcare software. Whereas the past decades of software engineering created innovators and generalists, the coming ones will be all about specialists with deep domain and functional expertise.
Finally, the study found that it is not possible to assert that metrics distribution generated from agile methodologies is related to buy the quality of software. At XP , there were two studies dealing with testing in agile development. This struggle is especially strong for companies adopting continuous delivery of software. The researchers conducted a multiple case study involving five software development sites, coming from large software companies. The research question of the study was "How can we visualize end-to-end testing activities in order to support the transformation towards continuous integration?
Each interviewed group comprised of 5 to 6 people, and lasted about two hours. The results showed several challenges regarding the verification and validation of the systems produced. These challenges were an end-to-end overview of testing companies, a significant duplication of testing efforts, long and slow feedback loops among the stakeholders, quality attributes tested too late in the process, and ad-hoc improvement efforts in terms of tactics.
In order to cope with these challenges, the researchers developed a visualization technique called CIViT to show the testing activities performed around the product. CIViT is concerned with four types of testing, namely the new functionality, legacy functionality, quality attributes, and edge cases. Edge cases testing is concerned with testing unlikely or weird situations, which were discovered after significant investigative effort. The motivation of the CIViT model was performed with respect to the previously defined challenges.
The validation confirmed that the model serves as a solution to the lack of a holistic end-to-end understanding of the testing activities and their periodicity. The model also enabled the organization's to identify how to change their testing activities and what were the implications of such changes. The authors reported that traditional scripted testing is a very common process. However, it has been claimed that the use of rigorous scripted testing is not common. The main reason is that the recommendation of every scenario of a test is a too much time-consuming activity.
Exploratory testing, on the other hand, has become popular in the agile communities. In exploratory testing, the tests are not planned and defined in advance. Instead, they are dynamically designed, executed, and modified. The authors formulated the research question of the study as "How does a hybrid testing process affect testing quality as compared to scripted testing and exploratory testing?
The researchers developed a simple application, and several defects were introduced in its release versions. The participants tested the application using the three different approaches. The results of the issue detections indicate that hybrid testing is more effective in defect section then scripted testing but less effective than exploratory testing. Lean was not a central theme at XP There were only two papers dealing with lean and agile development.
However, it is difficult to eliminate waste from knowledge-based work. A smooth flow is difficult to be achieved for many.monitoring.devops.indosystem.com/new-and-future-developments-in.php
Agile Software Development
While waste still exists in software development, the researchers argued that it is more appropriate to focus on impediments to flow instead. One reason is that "waste" is an emotive topic in teams and organizations, which is difficult to be discussed. A focus on flow, however, arguably provides a better catalyst for continuous improvement within knowledge-related work. The researchers presented a framework of nine impediment categories coming from the literature. The nine categories of impediments to flow are 1 extra features, 2 delays, 3 handovers, 4 failure demand, 5 writing progress, 6 context switching, 7 unnecessary motion, 8 extra processes, and 9 unmet human potential.
Extra features are those added features, without at valid hypothesis.
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Handovers happened whenever incomplete work must be handed over. Unnecessary motion refers to the movement of people, work, or knowledge that creates inefficiencies. The authors argued that while diversity in general can improve the team performance, the diversity and personal values could lead to conflict and lower productivity.
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While it is easy to understand that integrating culture and value into the software development process is a way to enhance the developer experience, it is difficult to understand values themselves. Therefore, it is important to understand the theoretical structure of values. The researchers employed a quantitative survey approach, investigating the structure of the lean and agile value system among software developers, the relationship between the lean and agile value system and the general human value system, and the relationship between the lean and agile value system and individual personality.
The responses of 57 participants were analyzed using agglomerative hierarchical clustering and non-metric multidimensional scaling. Ten clusters emerged from the data analysis. The first cluster represents the view that software developers should focus on technical work and should not deal with work belonging to stakeholders and managers.
The second cluster represents valuing flexibility in task execution. The third cluster concerned the values of planning and preparation, which is in conflict with the agile avoidance of long-term planning. The fourth cluster represents the belief of strict adherence to processes, which is preferred to having teams continuously discussing about what to do next.
The fifth cluster emphasizes discipline, which mostly refer to sticking to anything, which has been previously agreed. The sixth cluster is about giving value to people. However, the cluster is more about responsiveness and knowledge of contractual obligations then about the team well being.
The seventh cluster is about self-organization and responsiveness.
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Developers value the freedom to organize, e. The eight cluster emphasizes valuing the purpose of the work of people and their roles. The ninth cluster is about a desire for uncertainty reduction. It is related to the desire to base action on evidence rather than unjustified orders. The tenth cluster reflects valuing close, collaborative work. The eleventh cluster is concerned about customer involvement. At a broader view, the cluster deals about involving all the stakeholders, e.
Empirical Software Engineering
A two dimensional scaling of the responses related to the human values and the lean and agile values clusters showed that there are two different structures. Similarities exist on a continuum regarding the type of decision-making, control, and ambition. For example, valuing individual decision-making and self-enhancements was consistent with the self-focus in universal values. Collective focus was congruent with collaborative decision-making. Finally, the results showed that there might be a weak relationship between the clusters and personality.
XP offered a substantial space to short papers, which were published in the conference proceedings. Eight short papers were accepted for presentation and publication. They recruited participants, who answered 12 questions about their companies, the possible usage of agile methodologies or practices, and the technologies for web application development. The strongest preference was for Scrum, followed by eXtreme programming. The three most used agile practices were openwork area, daily standup meetings, and user stories.
The least employed practice was pair programming. The most frequent answers when dealing with tools were Jira, BugZilla, and none. Given the high number of participants who are not using any tools, the authors argue that there are still many hard-core agilists who prefer to use tangible artifacts rather than electronic tools.
Seventy percent of the participants were not certified agilists. This is not surprising, because the authors defined web applications as systems making use of Web browsers, including mobile apps. CMS typically fall only under the category of websites.
Finally, the most employed database among the respondents was MySQL. Datta et al. The law states that "given enough eyeballs, all bugs are shallow". This means that every problem in a software system is easily defined and fixed given a large enough number of people working on the system or observing it. The researchers wanted to see if there is empirical evidence supporting the claim.
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They studied the Android bug reports data. For each bug, they calculated the resolution time as the number of days between the bug opening and its closure. The researchers retained in the data set only those bugs, which had a resolution time of one year or less, because they assumed that bugs with longer resolution time could not receive enough developer attention. The final data set consisted of bugs and 73 developers assigned to those bugs. The researchers built the network connecting those developers and the bugs.
A higher betweenness the likelihood of a developer to be in a position to broker the interaction of others was related to an increased resolution time. Therefore, the more a developer is connected to other developers through the ownership of similar bugs, the more the developer is likely to be more deeply embedded in the development system, which was found to facilitate resolution of the bugs owned by that developer. However, the more involved a developer is in brokering interactions between other developers, the more likely the developer might become more distracted, leading to an higher resolution time.
The researchers omit in the limitations of the study that the closure date of a bug does not always or necessarily represent the date when the bug is resolved. Especially in large projects such as Android, there might be long discussions after a fix is released before the bug can be considered fixed. Additionally, the authors did not mention how they dealt with issue entries that could be closed and opened again. Therefore, the calculation of the bug resolution time might present some issues. The coding dojo is a technique for continuous learning and training coming from martial arts.
It was created for supporting the development of skills through repetitive practice of coding and social interaction. The randori is a dojo format, where pairs work in time-boxed rounds. A driver and a navigator compose the pairs, and they work like in pair programming. At the end of each time box, the driver moves to the audience, the navigator becomes the driver, and a third person from the audience becomes the navigator.
That is, dynamic pairs perform the learning in front of an audience. The researchers wanted to explain and characterize randori dojos. They obtained a set of twelve recordings made at a randori dojo organized in the UK. The results of the analysis of the recordings showed that the running of a randori dojo is way different than how a randori dojo is described in theory.
For example, the majority of the dojo was filled up with discussions between the driver and the navigator, and the pair and the audience. The chosen solutions caused the participants to discuss about pro and contra of those solutions, and many of them will often not familiar with the proposed solutions.
The development target where not met at the end of the session. However, the participants evaluated the experience as a positive learning experience. The dojo rarely exemplified good practice, but there were occasions of this and several discussions. In particular, the authors argued that it is important to know who knows what in the context of software projects.
They wanted to answer the question "how do agile team members depend on each other in locating expertise in their teams? The category "locating expertise" emerged from the data analysis, describing how team members identify the relevant expertise in teams. Four ways to identify expertise were found. Frequent and effective communication can help to determine when a team member has desirable expertise.
Working closely together provides opportunities for team members to identify and confirm people expertise. Self-identified expertise needs to be declared by agile team members, but the declaration must be followed by concrete impact. The last identified way to find expertise in agile teams is using an expertise directory. That is, some companies develop a skills database, in order to let people find competent team members. In particular, the study aimed to understand if refactoring practices were related to clusters in Java software.
They built networks of the examined software systems, by observing the relationships between classes. For every release of the software, the researchers tried to understand if classes are more connected with each other after undergoing a refactoring operation on not. They compared the number of clusters formed by refactored classes with the average number of clusters formed by random selection of them. The results showed that randomly selected classes are poorly coupled.
Therefore, if a class needs refactoring, there is a high chance that a connected class needs refactoring, as well. Power sees teams and organizations as complex adaptive systems. He wrote that self-organization in complex adaptive systems evolves through a set of simple rules. Power discussed how social contract theory, or how people in a social system have got rights and benefits determined by the system itself, applies to agile teams and organizations. The author argued that human systems dynamics provides a model for understanding self-organization in human existence.
Kettunen argued that agile software development teams are always striving for high performance. His paper proposed a performance analysis approach for agile software organizations. In particular, the researcher aimed to provide means to distinguish high performing software teams and measures to establish performance in organizational transformations. Although there is no universally recipe for creating and improving performance in software teams, such means stem from the processes, tools, organization, and the people in the team.
In the case of software organizations, the agile transformations are the adoption of agile software development methods and principles. However, there are no models for realizing such transformations, especially for large-scale software enterprises. Kettunen proposed three dimensions regarding team-based transformation management. The first one has the company as scope and emphasis on goals.
The related transformation traits are the needs and goal attainment strategies. The measures are the rating of drivers and the goals, while the instrument is the Agility Profiler Kettunen, The transformation traits are in team diversity portfolio coordination and alignment. The measures are the team performance positioning, or key capabilities, while the instrument is the Orientation Frame Kettunen,