Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring of Technical Debt should be prioritized with respect to developing features or fixing bugs.Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review of 557 unique papers published until 2019, following a consolidated methodology applied in software engineering. We included 44 primary studies.Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and proposing optimization regarding different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We present an impact map of such factors. However, there is a lack of empirical and validated set of tools.Conclusion. We observed that Technical Debt prioritization research is preliminary and there is no consensus on what the important factors are and how to measure them. Consequently, we cannot consider current research\ua0conclusive. In this paper, we therefore outline different directions for necessary future investigations

Sentiment Analysis Naive Bayes Method on SatuSehat Application

The SatuSehat application is an application that provides health services to users. This application is a development of the PeduliLindungi application which is used to handle vaccination history in the new normal era. Therefore, it is important to classify user reviews into positive and negative sentiments using the Naïve Bayes method. The use of this method because it can produce a model that is quite accurate and effective. The results of data collection in this study were 25,000 of which 18,359 were negative and 6,641 were positive. The results of the Naïve Bayes accuracy test are 97% with negative sentiment results, namely precision has a value of 98%, recall has a value of 98% and f1-score has a value of 98%, while positive sentiment results, namely precision has a value of 94%, recall has a value of 94 % and f1-score has a value of 94%. This study aims to classify user reviews of the SatuSehat application into positive and negative sentiments and assess the performance of the Naïve Bayes method regarding public opinion on the use of the SatuSehat application based on reviews from the Google Playstore application

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

IT portfolio attributes and investment choices

Many Chief Information Officers (CIOs) and senior executives face the challenge of finding the appropriate IT resource allocation to meet enterprise strategic goals across multi-organizational units. To address this problem, my dissertation opens the black box of enterprise strategic IT resource allocation by examining the prioritization and selection of IT investment choices (i.e., IT initiatives). Since IT Portfolio Management (ITPM) involves making applicable decisions to achieve a firm’s strategic objectives by fine-tuning budgeted costs and returns as business conditions change, my dissertation examines an important class of IS decision problems: IT portfolio attributes and investment choices. My research addresses how a firm can systematically profile numerous IT portfolios and provide theoretical insights into the components of the optimal solution. Based on design science, my specialized method incorporates mathematical optimization and computational experiments and combines real-world data using the Monte Carlo approach to simulate the experimental data. Consequently, by combining the suggested IT portfolio attributes while addressing a variety of ITPM-related issues, the main contribution for my research is a new ITPM-related methodology built on three proposed ITPM models/techniques: (1) optimal efficiency across multi-organizational levels/units simultaneously; (2) the most qualified IT portfolio selection that incorporates decision-makers’ risk tolerance levels; and (3) accurately estimating the current financial standing of each project in a portfolio of IT projects over the project’s full lifecycle. By applying the proposed ITPM-related methodology with illustrative examples, I develop theoretical propositions based on my main findings

DevOps for Digital Leaders

DevOps; continuous delivery; software lifecycle; concurrent parallel testing; service management; ITIL; GRC; PaaS; containerization; API management; lean principles; technical debt; end-to-end automation; automatio

Hybrid deep neural networks for mining heterogeneous data

In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity. The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and heterogeneous meta data are modeled. Detecting Copy Number Variations (CNVs) in genetic studies is used as a motivating example. A CNN-DNN blended neural network is proposed to authenticate CNV calls made by current state-of-art CNV detection algorithms. It utilizes hybrid deep neural networks to leverage both scatter plot image signal and heterogeneous numerical meta data for improving CNV calling and review efficiency. The second part of this dissertation deals with data of various frequencies or scales in time series data analysis, the second kind of data heterogeneity. The stock return forecasting problem in the finance field is used as a motivating example. A hybrid framework of Long-Short Term Memory and Deep Neural Network (LSTM-DNN) is developed to enrich the time-series forecasting task with static fundamental information. The application of the proposed framework is not limited to the stock return forecasting problem, but any time-series based prediction tasks. The third part of this dissertation makes an extension of LSTM-DNN framework to account for both temporal and spatial dependency among variables, common in many applications. For example, it is known that stock prices of relevant firms tend to fluctuate together. Such coherent price changes among relevant stocks are referred to a spatial dependency. In this part, Variational Auto Encoder (VAE) is first utilized to recover the latent graphical dependency structure among variables. Then a hybrid deep neural network of Graph Convolutional Network and Long-Short Term Memory network (GCN-LSTM) is developed to model both the graph structured spatial dependency and temporal dependency of variables at different scales. Extensive experiments are conducted to demonstrate the effectiveness of the proposed neural networks with application to solve three representative real-world problems. Additionally, the proposed frameworks can also be applied to other areas filled with similar heterogeneous inputs

Opportunities and Challenges in Commissioning Materiality-Driven Sustainability Reporting Towards the SDGs: The Case of Cadeler A/S

Frequently and recently tightening and expanding sustainability reporting policies and requirements can pose significant administrative burdens on SMEs upholding a strong culture of accountability to their stakeholder network. This seminal case study examines how a Danish offshore wind farm commissioner can efficiently (1) navigate towards credibility in and (2) derive actionable insights from their sustainability (reporting) integration trajectory by capitalizing on the increasingly emphasized materiality principle. Group-based Fuzzy AHP and Textual Analysis aim to excavate and assess senior managers’ and external stakeholders’ preferences based on the GRI Standards and the UN’s SDG targets. Internal priorities emphasize safety, compliance, and profitability, whereas external stakeholders’ and their groups’ priorities exhibit mixed findings on their type and extent of alignment with the former. Content elements assigned higher relative importance tend to be more robust to changes in decision-makers’ uncertainty and verbal bias. The author confirms that a simplicity-informativeness trade-off tends to be driven by stakeholder grouping and that a data-driven, subject-based, and objectifying approach should be complemented with context, managerial judgment, and process iteration. Keywords: Sustainability; materiality; prioritization; credibility; actionability.Frequently and recently tightening and expanding sustainability reporting policies and requirements can pose significant administrative burdens on SMEs upholding a strong culture of accountability to their stakeholder network. This seminal case study examines how a Danish offshore wind farm commissioner can efficiently (1) navigate towards credibility in and (2) derive actionable insights from their sustainability (reporting) integration trajectory by capitalizing on the increasingly emphasized materiality principle. Group-based Fuzzy AHP and Textual Analysis aim to excavate and assess senior managers’ and external stakeholders’ preferences based on the GRI Standards and the UN’s SDG targets. Internal priorities emphasize safety, compliance, and profitability, whereas external stakeholders’ and their groups’ priorities exhibit mixed findings on their type and extent of alignment with the former. Content elements assigned higher relative importance tend to be more robust to changes in decision-makers’ uncertainty and verbal bias. The author confirms that a simplicity-informativeness trade-off tends to be driven by stakeholder grouping and that a data-driven, subject-based, and objectifying approach should be complemented with context, managerial judgment, and process iteration. Keywords: Sustainability; materiality; prioritization; credibility; actionability