Evaluating the impact of caching on the energy consumption and performance of progressive web apps

Context. Since today mobile devices have limited battery life, the energy consumption of the software running on them can play a strong role with respect to the success of mobile-based businesses. Progressive Web Applications (PWAs) are built using common web technologies like HTML, CSS, and JavaScript and are commonly used for providing a better user experience to mobile users. Caching is the main technique used by PWA developers for optimizing network usage and for providing a meaningful experience even when the user's device is offline. Goal. This paper aims at assessing the impact of caching on both the energy consumption and performance of PWAs. Method. We conducted an empirical experiment targeting 9 real PWAs developed by third-party developers. The experiment is designed as a 1 factor-2 treatments study, with the usage of caching as the single factor and the status of the cache as treatments (empty vs populated cache). The response variables of the experiment are (i) the energy consumption of the mobile device and (ii) the page load time of the PWAs. The experiment is executed on a real Android device running the Mozilla Firefox browser. Results. Our results show that PWAs do not consume significantly different amounts of energy when loaded either with an empty or populated cache. However, the page load time of PWAs is significantly lower when the cache is already populated, with a medium effect size. Conclusions. This study confirms that PWAs are promising in terms of energy consumption and provides evidence that caching can be safely exploited by PWA developers concerned with energy consumption. The study provides also empirical evidence that caching is an effective technique for improving the user experience in terms of page loading time of PWAs

Discovering Software Process Deviations Using Visualizations

Peer reviewe

The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a widespread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled

Systematic literature review of prediction techniques to identify work skillset

A mismatch of skillsets is a main cause to the unemployment in Malaysia. It is a situation where the level and work skillset that are available do not match the market demands and the individual does not know how to identify the skills that they have. To deal with this problem, prediction techniques is used to assist in identifying work-appropriate skills for individual. Thus, a systematic literature review (SLR) on predicting work skillsets using prediction techniques is proposed. The aim of this study is to give an overview on the prediction techniques that have been used to predict work skillset and the accuracy of the techniques. We use SLR to identify 383 prediction techniques studies for identifying skills published from 2014 to 2019. As a result, 9 studies report adequate information and methodology according to our criteria and apply. From the studies, classification techniques are used for predicting work skillset. The algorithms used is Random Forest with precision is 99%. From this study, a future study will be conducted by developing a prediction model to help identifying appropriate work skillsets to meet current needs and identifying the levels of skills they have. The significant of this study is the researchers are able to understand deeply about the prediction techniques used to identify work skillset and the accuracy of the techniques used

Kuksa*: Self-Adaptive Microservices in Automotive Systems

In pervasive dynamic environments, vehicles connect to other objects to send operational data and receive updates so that vehicular applications can provide services to users on demand. Automotive systems should be self-adaptive, thereby they can make real-time decisions based on changing operating conditions. Emerging modern solutions, such as microservices could improve self-adaptation capabilities and ensure higher levels of quality performance in many domains. We employed a real-world automotive platform called Eclipse Kuksa to propose a framework based on microservices architecture to enhance the self-adaptation capabilities of automotive systems for runtime data analysis. To evaluate the designed solution, we conducted an experiment in an automotive laboratory setting where our solution was implemented as a microservice-based adaptation engine and integrated with other Eclipse Kuksa components. The results of our study indicate the importance of design trade-offs for quality requirements' satisfaction levels of each microservices and the whole system for the optimal performance of an adaptive system at runtime

Comparing Four Approaches for Technical Debt Identification

Background: Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long term goals (e.g., quick-and-dirty implementation to reach a release date vs. a well-refactored implementation that supports the long term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, i.e. if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD "interest" that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Aims: Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD "interest". Method: We selected four different TD identification techniques (code smells, automatic static analysis (ASA) issues, grime buildup, and modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change proneness). Results: The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed coupling and modularity violations were co-located in classes with higher defect proneness. We also observed a strong relationship between modularity violations and change proneness. Conclusions: Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicator

Technical debt and waste in non-functional requirements documentation:an exploratory study

Background: To adequately attend to non-functional requirements (NFRs), they must be documented; otherwise, developers would not know about their existence. However, the documentation of NFRs may be subject to Technical Debt and Waste, as any other software artefact. Aims: The goal is to explore indicators of potential Technical Debt and Waste in NFRs documentation. Method: Based on a subset of data acquired from the most recent NaPiRE (Naming the Pain in Requirements Engineering) survey, we calculate, for a standard set of NFR types, how often respondents state they document a specific type of NFR when they also state that it is important. This allows us to quantify the occurrence of potential Technical Debt and Waste. Results: Based on 398 survey responses, four NFR types (Maintainability, Reliability, Usability, and Performance) are labelled as important but they are not documented by more than 22% of the respondents. We interpret that these NFR types have a higher risk of Technical Debt than other NFR types. Regarding Waste, 15% of the respondents state they document NFRs related to Security and they do not consider it important. Conclusions: There is a clear indication that there is a risk of Technical Debt for a fixed set of NFRs since there is a lack of documentation of important NFRs. The potential risk of incurring Waste is also present but to a lesser extent

An experimental evaluation of the understanding of safety compliance needs with models

Proceedings of: 36th International Conference on Conceptual Modeling, ER 2017, Valencia, Spain, November 6–9, 2017Context: Most safety-critical systems have to fulfil compliance needs specified in safety standards. These needs can be difficult to understand from the text of the standards, and the use of conceptual models has been proposed as a solution. Goal: We aim to evaluate the understanding of safety compliance needs with models. Method: We have conducted an experiment to study the effectiveness, efficiency, and perceived benefits in understanding these needs, with text of safety standards and with UML object diagrams. Results: Sixteen Bachelor students participated in the experiment. Their average effectiveness in understanding compliance needs and their average efficiency were higher with models (17% and 15%, respectively). However, the difference is not statistically significant. The students found benefits in using models, but on average they are undecided about their ease of understanding. Conclusions: Although the results are not conclusive enough, they suggest that the use of models could improve the understanding of safety compliance needs.The research leading to this paper has received funding from the AMASS project (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262) and the AMoDDI project (Ref. 11130583). We also thank the subjects that participated in the experiment

The unfulfilled potential of data-driven decision making in agile software development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a wide-spread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled