Optimization Coaching for JavaScript (Artifact)

This artifact is based on our prototype optimization coach for the SpiderMonkey (https://developer.mozilla.org/en-US/docs/Mozilla/Projects/SpiderMonkey) JavaScript engine. An optimization coach is a performance tool that aims to provide programmers with insight into how their compiler optimizes their programs and to help them better harness the optimization process. It does so by reporting optimization near misses, i.e., reports of optimizations that the compiler did not apply, but could apply if the program were to be modified slightly. This artifact provides the necessary environment, programs and data to repeat our experiments, and to allow readers to run our tool on JavaScript programs of their choic

Optimization Coaching for JavaScript

The performance of dynamic object-oriented programming languages such as JavaScript depends heavily on highly optimizing just-in-time compilers. Such compilers, like all compilers, can silently fall back to generating conservative, low-performance code during optimization. As a result, programmers may inadvertently cause performance issues on users\u27 systems by making seemingly inoffensive changes to programs. This paper shows how to solve the problem of silent optimization failures. It specifically explains how to create a so-called optimization coach for an object-oriented just-in-time-compiled programming language. The development and evaluation build on the SpiderMonkey JavaScript engine, but the results should generalize to a variety of similar platforms

Actionable Program Analyses for Improving Software Performance

Nowadays, we have greater expectations of software than ever before. This is followed by constant pressure to run the same program on smaller and cheaper machines. To meet this demand, the application’s performance has become the essential concern in software development. Unfortunately, many applications still suffer from performance issues: coding or design errors that lead to performance degradation. However, finding performance issues is a challenging task: there is limited knowledge on how performance issues are discovered and fixed in practice, and current performance profilers report only where resources are spent, but not where resources are wasted. The goal of this dissertation is to investigate actionable performance analyses that help developers optimize their software by applying relatively simple code changes. To understand causes and fixes of performance issues in real-world software, we first present an empirical study of 98 issues in popular JavaScript projects. The study illustrates the prevalence of simple and recurring optimization patterns that lead to significant performance improvements. Then, to help developers optimize their code, we propose two actionable performance analyses that suggest optimizations based on reordering opportunities and method inlining. In this work, we focus on optimizations with four key properties. First, the optimizations are effective, that is, the changes suggested by the analysis lead to statistically significant performance improvements. Second, the optimizations are exploitable, that is, they are easy to understand and apply. Third, the optimizations are recurring, that is, they are applicable across multiple projects. Fourth, the optimizations are out-of-reach for compilers, that is, compilers can not guarantee that a code transformation preserves the original semantics. To reliably detect optimization opportunities and measure their performance benefits, the code must be executed with sufficient test inputs. The last contribution complements state-of-the-art test generation techniques by proposing a novel automated approach for generating effective tests for higher-order functions. We implement our techniques in practical tools and evaluate their effectiveness on a set of popular software systems. The empirical evaluation demonstrates the potential of actionable analyses in improving software performance through relatively simple optimization opportunities

Käytettävyyden ja kehityksen modernisointi mikropalveluilla

Vanhat ohjelmistojärjestelmät, joilla tarkoitetaan vanhoja ja vanhentuneita ohjelmistoja joita on tehty vanhentuneilla työskentelytavoilla, ovat todellisuus jonka kanssa suurin osa ohjelmistokehitysyrityksistä joutuvat kamppailemaan. Vanhat työskentelytavat ja teknologiat aiheuttavat usein ohjelmiston kehityksen ja julkaisun hidastumista, sillä niiden jatkuvassa käytössä voi piillä yhteensopivuus, turvallisuus, skaalautuvuus sekä ekonomisia ongelmia, muiden ongelmien muassa. Ohjelmistojärjestelmien modernisointi, uudelleensuunnittelu ja refaktorointi voivat lievittää vanhoista järjestelmistä nousevia ongelmia, oli se sitten työskentelytapojen muutoksella, teknologioiden päivityksellä tai ohjelmistoalustojen vaihdolla. On olemassa monia teknologioita ja metodeja jotka voivat helpottaa ohjelmistojärjestelmien modernisointia, mukaanlukien siirto käyttämään erilaista arkkitehtuuria, uusien teknologioiden käyttöönotto ja ohjelmistokehityksen tapojen vaihto. Näillä teknologioilla ja metodeilla, ja modernisaatiolla yleensäkkin, on omat riskinsä ja haasteensa, jotka tulee ottaa huomioon onnistuneen modernisaation aikaansaamiseksi; Nämä strategiset huomiot ovat avaintekijöitä modernisaatiossa. Tämä opinnäytetyö tutkii ohjelmistojen modernisaatiota yleisellä tasolla kirjallisuusarvostelun kautta, ja käyttää tietyn yrityksen tapaustutkimuksen dataa, joka on kerätty kyselyjen ja yhtiön lokien kautta, katsoen mitä teknologioita, konsepteja ja strategioita tarvitaan onnistuneeseen modernisaatioon, ja mitä vaikutuksia modernisaatiolla on modernisoitavaan ohjelmistojärjestelmään loppukäyttäjien sekä ohjelmistokehittäjien näkökulmasta. Tämän tutkimuksen lopputulos paljastaa miksi modernisaatio on monimutkainen aihe jossa on monia haasteita, mutta joka samaan aikaan tarjoaa monia hyötyjä modernisoitavalle ohjelmistojärjestelmälle. Näitä tuloksia on parasta käyttää ohjeina siihen, mihin ongelmiin kannattaa keskittyä modernisoinnin aikana, pitäen mielessä tapaustutkimuksen rajoitetun soveltamisalan.Legacy software systems, which refers to old and likely outdated software applications and practices, are a reality that most software development companies have to contend with. Old practices and technologies are often at fault for slowing down development and deployment of software, as they can have compatibility, security, scalability and economic issues with their continued use, among other issues. Software modernization, reengineering and refactoring can alleviate the issues stemming from legacy systems, whether it be in the form of altering practices, updating technologies or changing platforms. There are many technologies and methods that can facilitate the modernization of a software system, including a move to using different architectures, specific newer technologies and changing the methods of working and developing the software system. These technologies and methods, and modernization in general, come with their own risks and challenges that must be considered for a successful modernization to take place; These strategic considerations are a key factor in modernization. This thesis will explore software modernization in general through literature reviews and as a case study for a specific company using data from surveys and the case company’s logs, with a look into the technologies, concepts and strategies required for a successful modernization, and what kinds of effects modernization can have on the software system being modernized, both from a user perspective as well as from a developer perspective. The end-result of this exploration reveals that modernization is a complex subject with many challenges, but that also offers benefits to the software system being modernized. These results are best used as a guideline on what issues should be concentrated on during modernization, with a mindful consideration for the limited scope of the case study represented within

Automatic Generation of Personalized Recommendations in eCoaching

Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

How to Make chatbots productive – A user-oriented implementation framework

Many organizations are pursuing the implementation of chatbots to enable automation of service processes. However, previous research has highlighted the existence of practical setbacks in the implementation of chatbots in corporate environments. To gain practical insights on the issues related to the implementation processes from several perspectives and stages of deployment, we conducted semi-structured interviews with developers and experts of chatbot development. Using qualitative content analysis and based on a review of literature on human computer interaction (HCI), information systems (IS), and chatbots, we present an implementation framework that supports the successful deployment of chatbots and discuss the implementation of chatbots through a user-oriented lens. The proposed framework contains 101 guiding questions to support chatbot implementation in an eight-step process. The questions are structured according to the people, activity, context, and technology (PACT) framework. The adapted PACT framework is evaluated through expert interviews and a focus group discussion (FGD) and is further applied in a case study. The framework can be seen as a bridge between science and practice that serves as a notional structure for practitioners to introduce a chatbot in a structured and user-oriented manner

Breakthroughs and emerging insights from ongoing design science projects: Research-in-progress papers and poster presentations from the 11th international conference on design science research in information systems and technology (DESRIST) 2016. St. John, Newfoundland, Canada, May 23-25

This volume contains selected research-in-progress papers and poster presentations from DESRIST 2016 - the 11th International Conference on Design Science Research in Information Systems and Technology held during 24-25 May 2016 at St. John's, Newfoundland, Canada. DESRIST provides a platform for researchers and practitioners to present and discuss Design Science research. The 11th DESRIST built on the foundation of ten prior highly successful international conferences held in Claremont, Pasadena, Atlanta, Philadelphia, St. Gallen, Milwaukee, Las Vegas, Helsinki, Miami, and Dublin. This year's conference places a special emphasis on using Design Science to engage with the growing challenges that face society, including (but not limited to) demands on health care systems, climate change, and security. With these challenges in mind, individuals from academia and industry came together to discuss important ongoing work and to share emerging knowledge and ideas. Design Science projects often involve multiple sub-problems, meaning there may be a delay before the final set of findings can be laid out. Hence, this volume "Breakthroughs and Observations from Ongoing Design Science Projects" presents preliminary findings from studies that are still underway. Completed research from DESRIST 2016 is presented in a separate volume entitled "Tackling Society's Grand Challenges with Design Science", which is published by Springer International Publishing, Switzerland. The final set of accepted papers in this volume reflects those presented at DESRIST 2016, including 11 research-in-progress papers and 4 abstracts for poster presentations. Each research-in-progress paper and each poster abstract was reviewed by a minimum of two referees. We would like to thank the authors who submitted their research-in-progress papers and poster presentations to DESRIST 2016, the referees who took the time to construct detailed and constructive reviews, and the Program Committee who made the event possible. Furthermore we thank the sponsoring organisations, in particular Maynooth University, Claremont Graduate University, and Memorial University of Newfoundland, for their financial support. We believe the research described in this volume addresses some of the most topical and interesting design challenges facing the field of information systems. We hope that readers find the insights provided by authors as valuable and thought-provoking as we have, and that the discussion of such early findings can help to maximise their impact

Approaches for Documentation in Continuous Software Development

It is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading ­– about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions