Further Investigation of the Survivability of Code Technical Debt Items

Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts. Empirical results on open-source systems demonstrated how code-smells, which are just one type of TDIs, are introduced and "survive" during release cycles. However, little is known about whether the results on the survivability of code-smells hold for other types of code TDIs (i.e., bugs and vulnerabilities) and in industrial settings. Goal: Understanding the survivability of code TDIs by conducting an empirical study analysing two industrial cases and 31 open-source systems from Apache Foundation. Method: We analysed 133,670 code TDIs (35,703 from the industrial systems) detected by SonarQube (in 193,196 commits) to assess their survivability using survivability models. Results: In general, code TDIs tend to remain and linger for long periods in open-source systems, whereas they are removed faster in industrial systems. Code TDIs that survive over a certain threshold tend to remain much longer, which confirms previous results. Our results also suggest that bugs tend to be removed faster, while code smells and vulnerabilities tend to survive longer.Comment: Submitted to the Journal of Software: Evolution and Process (JSME

The Impact of Ownership and Contribution Alignment on Code Technical Debt Accumulation

Software development organisations strive to maintain their effectiveness while the complexity of the systems they develop continues to grow. To tackle this challenge, organisations tend to be organised into small teams working with components that can be developed separately. Here, organisations must design their architecture and organisational structures in a way that enables communication and minimises dependencies, and helps teams reduce code and architectural degradation. Ensuring that each small independent team is responsible for components they primarily contribute is one approach to achieving this goal. This study aims at understanding the impact of ownership and contribution alignment on accumulation of code technical debt and how abrupt changes in team constellation affect teams' effectiveness in managing TD. We conducted an embedded case study in a company developing a very large software system, analysing ten components belonging to 1 team. During the studied period, the team was split into two, and the components owned by them were distributed between the two new teams. With high degrees of contribution alignment, we noticed a negative correlation between contribution alignment and TD Density before the team split. In 4 components, this correlation is statistically significant. This means a higher contribution alignment degree implies a lower TD Density. After the split, we observe a negative correlation in 3 components. The positive correlation observed in the other 5 components could be attributed to low contribution alignment, leading to difficulties in managing TD. Our findings suggest that contribution alignment can be important in controlling TD in software development organisations. Making teams responsible for the quality of components they have more expertise over and minimising dependencies between teams can help organisations mitigate the growth of TD.Comment: Submitted to Transactions on Software Engineering (TSE

Assets in Software Engineering: What are they after all?

During the development and maintenance of software-intensive products or services, we depend on various assets. These assets are important to the feasibility of the project and influence product's final quality. However, despite their central role in the software development process, little thought is yet invested into what assets eventually are, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the resulting notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets' characteristics such as value and persistence. We discuss what asset degradation is, its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. With our work, we aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.Comment: Manuscript submitted to the Journal of Systems and Softwar

Taxing Collaborative Software Engineering

The engineering of complex software systems is often the result of a highly collaborative effort. However, collaboration within a multinational enterprise has an overlooked legal implication when developers collaborate across national borders: It is taxable. In this short article, we discuss the unsolved problem of taxing collaborative software engineering across borders. We (1) introduce the reader to the basic principle of international taxation, (2) identify three main challenges for taxing collaborative software engineering, and (3) estimate the industrial significance of cross-border collaboration in modern software engineering by measuring cross-border code reviews at a multinational software company.Comment: 7 pages, 3 figure

Towards Understanding Assets in Software Engineering

The development of software products is a massive undertaking, and organisations have to manage all artefacts involved in the process. Managing such artefacts that, in many cases, become crucial assets is important for success. Recognising assets and letting them (unintentionally) degrade can result in maintainability problems. Thus, there is a need to create a structured and organised body of knowledge that can guide practitioners and researchers to deal with the assets during the product/service life-cycle. This includes, but is not limited to, what steps are needed to understand the assets’ degradation, investigating and examining the existing methods and metrics on how to estimate degradation and understanding the implication of assets’ value and degradation. This licentiate’s main objective is contributing to the software engineering field by providing a different perspective on assets focusing on assets’ value for the organisation. We have used literature reviews, focus groups, case study, and sample study to address this objective. The collected data is from peer-reviewed work, collaboration with five company partners, and 31 OSS from Apache Foundation. First, we have defined the concept and terminology in a position paper. We havecreated an asset management taxonomy based on a literature review and focus groups– fours focus groups conducted in 2019 with 29 participants. The extracted assets represent not only the stages of software development, from requirements to verificationand validation, but also operational and organisational perspectives. The taxonomy wascreated to be extendable as the field evolves and matures. Then, we have performed a more in-depth investigation of selected asset types. As a part of studying assets, in a case study, we present the impact of bug-fixing,refactorings, and new development to investigate how source code degrades. In anothersample study, we examine the longevity of specific source-code related issues in 31OSS from Apache Foundation using statistical analysis. The work done in this licentiate includes: defining the asset concept and relatedterminology, identifying assets and creating a taxonomy of assets, presenting the preliminary investigation of tools and methods to understand source-code and architecturerelated asset degradation. We conclude that a good understanding of the relevant assets for the inception,planning, development, evolution, and maintenance of software-intensive products andservices is necessary to study their value degradation. Our work builds on currentmethods and details the underlying concepts attempting to homogenise definitions andbring the areas of assets and degradation together. A natural progression of our workis to investigate the measurements to evaluate the degradation of assets. This licentiate thesis starts investigating the value degradation of source-code related assets. We planto continue investigating the degradation of architecture in our future work

Towards Understanding Assets in Software Engineering

The development of software products is a massive undertaking, and organisations have to manage all artefacts involved in the process. Managing such artefacts that, in many cases, become crucial assets is important for success. Recognising assets and letting them (unintentionally) degrade can result in maintainability problems. Thus, there is a need to create a structured and organised body of knowledge that can guide practitioners and researchers to deal with the assets during the product/service life-cycle. This includes, but is not limited to, what steps are needed to understand the assets’ degradation, investigating and examining the existing methods and metrics on how to estimate degradation and understanding the implication of assets’ value and degradation. This licentiate’s main objective is contributing to the software engineering field by providing a different perspective on assets focusing on assets’ value for the organisation. We have used literature reviews, focus groups, case study, and sample study to address this objective. The collected data is from peer-reviewed work, collaboration with five company partners, and 31 OSS from Apache Foundation. First, we have defined the concept and terminology in a position paper. We havecreated an asset management taxonomy based on a literature review and focus groups– fours focus groups conducted in 2019 with 29 participants. The extracted assets represent not only the stages of software development, from requirements to verificationand validation, but also operational and organisational perspectives. The taxonomy wascreated to be extendable as the field evolves and matures. Then, we have performed a more in-depth investigation of selected asset types. As a part of studying assets, in a case study, we present the impact of bug-fixing,refactorings, and new development to investigate how source code degrades. In anothersample study, we examine the longevity of specific source-code related issues in 31OSS from Apache Foundation using statistical analysis. The work done in this licentiate includes: defining the asset concept and relatedterminology, identifying assets and creating a taxonomy of assets, presenting the preliminary investigation of tools and methods to understand source-code and architecturerelated asset degradation. We conclude that a good understanding of the relevant assets for the inception,planning, development, evolution, and maintenance of software-intensive products andservices is necessary to study their value degradation. Our work builds on currentmethods and details the underlying concepts attempting to homogenise definitions andbring the areas of assets and degradation together. A natural progression of our workis to investigate the measurements to evaluate the degradation of assets. This licentiate thesis starts investigating the value degradation of source-code related assets. We planto continue investigating the degradation of architecture in our future work

Understanding Asset Degradation in Software Engineering

Background: As software is everywhere, and almost every company has nowadays a dependency on software, designing and developing software-intensive products or services has become significantly challenging and time-consuming. The challenges are due to the continuous growth of the size and complexity of software and the fast pace of change. It is important that software-developing organisations’ engineering practices adapt to the rising challenges by adopting well-engineered development activities. Organisations deal with many software artefacts, some of which are more relevant for the organisation. We define Software Assets as artefacts intended to be used more than once. Given softwared evelopment’s continuous and evolutionary aspect, the assets involved degrade over time. Organisations need to understand what assets are relevant and how they degrade to exercise quality control over software assets. Asset degradation is inevitable, and it may manifest in different ways.  Objective: The main objective of this thesis is: (i) to contribute to the software engineering body of knowledge by providing an understanding of what assets are and how they degrade; and (ii) to gather empirical evidence regarding asset degradation and different factors that might impact it on industrial settings.  Method: To achieve the thesis goals, several studies have been conducted. The collected data is from peer-reviewed literature and collaboration with five companies that included extracting archival data from over 20 million LOC and archival data from open-source repositories.  Results: The first contribution of this thesis is defining the concept of assets and asset degradation in a position paper. We aim to provide an understanding of software assets and asset degradation and its impact on software development.  Additionally, a taxonomy of assets is created using academic and industrial input. The taxonomy includes 57 assets and their categories. To further investigate the concept of asset degradation, we have conducted in-depth analyses of multiple industrial case studies on selected assets. This thesis presents results to provide evidence on the impact of different factors on asset degradation, including: (I) how the accumulation of technical debt is affected by different development activities; (ii) how degradation ‘survives’; and (iii) how working from home or the misalignment between ownership and contribution impacts the faster accumulation of asset degradation. Additionally, we created a model to calculate the degree of the alignment between ownership and contribution to code.  Conclusion: The results can help organisations identify and understand the relevant software assets and characterize their quality degradation. Understanding how assets degrade and which factors might impact their faster accumulation is the first step to conducting sufficient and practical asset management activities. For example, by engaging (i) proactively in preventing uncontrolled growth of degradation (e.g., aligning ownership and contribution); and (ii) reactively in prioritizing mitigation strategies and activities (focusing on recently introducing TD items)

Ameliyat sürelerinin güdümlü makine öğrenme tekniklerı ile tahmini.

There’s an ever increasing number of patients referred to healthcare facilities and hospitals. The healthcare facilities have two main options to deal with this situation. They have to either employ and acquire more resources or they should use the existing staff and resources more efficiently and effectively. The first option is not always feasible due to the fact that the healthcare facilities have limitations on both the staff they can employ and the resources they can acquire. Given the fact that these resources are expensive and extra resources provide diminishing returns, it is important to make the best use of resources available. Operating rooms and surgeons are the most expensive and scarce resources in hospitals; so it is crucial to optimize their performance and avoid under and over utilized operating rooms. The aim of this study is to employ supervised machine learning techniques and probabilistic graphical models to predict the duration of surgical operations using historical data. We have used a wide spectrum of different models ranging from regression methods, classification methods, and Bayesian Networks to predict the surgical operation durations. The models built based on Bayesian Networks, in general, produce more accurate results with lower errors. Naive Bayes, however, outperforms the other Bayesian-Network based models with an average accuracy of 66.9% and root mean square error of 998 seconds (16.6 minutes) from the true duration of the operation. Provided with accurate estimation of surgical operation durations, it is possible to build optimization models to utilize healthcare facility resources. This allows healthcare facilities’ managers to create tactical (medium term) plans and to increase efficient utilization of operating rooms and surgeons.M.S. - Master of Scienc

An Active Learning Approach

In order for software project management courses to be effective, they should be appropriately aligned to the learning outcomes. Common misalignments are caused by using an incorrect degree of realism or inappropriate support