Performance Comparison of CRUD Operations in IoT based Big Data Computing

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently.

Representational Learning Approach for Predicting Developer Expertise Using Eye Movements

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on short code snippets that do not represent how developers usually program in a realistic setting. A comparative analysis between representational learning and non-representational learning (Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest) is also presented. The results are obtained from an extensive set of experiments (with an 80/20 training and testing split) which show that representational learning (MLP) works well on our dataset reporting an average higher accuracy of 30% more for all tasks. Furthermore, a state-of-the-art method for feature engineering is proposed to extract features from the eye-tracking data. The average accuracy on all the tasks is 93.4% with a recall of 78.8% and an F1 score of 81.6%. We discuss the implications of these results on the future of automated prediction of developer expertise. Adviser: Bonita Shari

Predicting code comprehension: a novel approach to align human gaze with code using deep neural networks

The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades old and neglect the human aspect. Research has shown that considering how a developer reads and experiences the code can be an indicator of its quality. In our research, we built on these insights and designed, trained, and evaluated the first deep neural network that aligns a developer’s eye gaze with the code tokens the developer looks at to predict code comprehension and perceived difficulty. To train and analyze our approach, we performed an experiment in which 27 participants worked on a range of 16 short code comprehension tasks while we collected fine-grained gaze data using an eye tracker. The results of our evaluation show that our deep neural sequence model that integrates both the human gaze and the stimulus code, can predict (a) code comprehension and (b) the perceived code difficulty significantly better than current state-of-the-art reference methods. We also show that aligning human gaze with code leads to better performance than models that rely solely on either code or human gaze. We discuss potential applications and propose future work to build better human-inclusive code evaluation systems

Correlation of stress and physiological data

Stress is a mental pressure caused by demanding circumstances, tasks or environment that we live in our daily life. Long-term stress like Chronic stress has a longer negative emotional effect and in a long-term uncontrolled situation, it could damage health and prone to the huge risk of mental and cardiac diseases. Workplace stress is one of the major stress factors that affect the young working people. According to the World Organization for Stress and the American Institute of Stress, the number of patients with stressrelated diseases has been increasing at a drastic rate. Among all people, adults and working people have been reported as being highly affected by stress diseases. In this thesis, the stress of computer programmers is researched with the participants from software development professionals and students at the university. Their physiological data is examined to find the existence of such features that can signal the different stress levels which can be useful in developing stress aware systems. The physiological activity data is collected using an existing computer peripheral like mouse and keyboards whereas a popular statistical analysis method called Pearson correlation is used to inspect the correlation between stress and physiological data. Such features can be used to model a stress classifier in future which can help in the prediction of stress and provide assistance in mental and psychological well-being. As a process of organizing and conducting the research successfully, the research proceeds through a series of phases like planning, research on related fields, design an experiment, data collection and finally data analysis and interpreting the result

Expectation incongruence in music and code reading: The eye-tracking approach

Humans create and use different kinds of languages in order to store, view, and convey various types of information. Natural languages, such as English, allow people to communicate with each other in everyday and professional contexts. In contrast, symbolic languages, such as Western music notation or programming languages, enable people to make use of technical devices like musical instruments or computers. Research on the eye movements of expert musicians and programmers has revealed certain similarities in how these symbolic languages are read: unlike text reading, experts read music and code with more regressive eye movements. The current dissertation is the first project that explores music and code reading together. It focuses on one of the aspects of music and code reading that is equally important for both symbolic languages—the skill of working with unexpected information from a notation. In music and programming, this skill is especially required in tasks such as handling surprising melodic patterns and debugging, respectively. This dissertation had three main aims: (1) theoretical exploration of similarities and differences in creating expectations that help with pattern recognition in music and programming; (2) development (in music reading) and creation (in code reading) of research methodologies that can be applied in research on incongruent patterns; and (3) exploration of the cognitive processing of incongruent notation in experienced music readers and one experienced code reader. Surprising elements in familiar patterns hamper their recognition. Article I presents a theoretical exploration of the similarities and differences in building expectations that allow pattern recognition in music and programming. The proposed prediction model, which serves as the solution to Aim 1, includes three components that are common for both music and programming: (1) knowledge of language systems, (2) knowledge of meaning, and (3) knowledge of context. In addition, it also contains two components that differ for music and programming: (4) translation of information and (5) temporal and motor requirements. Experiments presented in this dissertation can be considered to be the first steps toward looking at certain components of the proposed prediction model in detail when prediction works normally (congruent notation) and when prediction is violated (incongruent notation). In order to study the reading of surprising incongruent patterns in music and code, special experimental settings, which provide the solution to Aim 2, were developed for music reading and created for code reading. Hence, the selected setup for the music reading study was based on a prior study (Penttinen et al., 2015), where incongruences were introduced in the “Mary Had a Little Lamb” melody and all music performances were temporarily controlled using a metronome. Experiment 1 developed this set-up by inserting incongruent notes into two different tonalities and by asking participants to play on a piano or sing from the notation. Thus, the music reading experiment focused on the first, second, fourth, and fifth components of the proposed model in music reading. It explored how the meaning of congruent and incongruent musical symbols is processed by experienced music readers. In addition, it also explored the translation of music information into two different performance ways (singing and playing piano) that have different motor requirements. Combining three different eye movement parameters allowed the researcher to describe different aspects of the cognitive processing of incongruent music reading—the temporal aspect, with the help of the eye-time span parameter (ETS), the cognitive effort aspect with the help of the mean pupil size parameter measured only in first-pass fixations, and the attention aspect with the help of the first-pass fixation duration. Experiment 2 on code reading was carefully designed on the basis of the music reading study. Consequently, incongruences were introduced in different parts of the familiar notation. The Bubble sort algorithm—a well-known sorting algorithm—was chosen as an analogue of the “Mary Had a Little Lamb” melody in programming. As in the music reading study, all code reading performances were temporarily controlled. The case code study provided some insights into the first and second components of the proposed model in programming by investigating how an experienced programmer reads sorting algorithms with and without surprising patterns. It particularly focuses on the phenomenon of an experienced reader overlooking the surprising pattern, which is considered to be the original one instead so-called proof-readers’ error. In addition, this study explored the issue of the unit of code reading analysis by comparing two different options: lines and elements. The study introduced saccade velocity as a parameter of cognitive effort for the incongruent code reading analysis. Research findings from these experimental studies provided the solution to Aim 3 and revealed that—in both music and code reading—incongruent patterns in the notation led to changes in fixation and cognitive effort parameters (pupil size and saccadic velocity). In contrast to code reading, strict temporal requirements for the processing of incongruence exist in music reading. The application of the eye-time span (ETS) parameter that describes the distance between the performer’s gaze and musical time, allowed the researcher to investigate the temporal aspect of incongruence processing in the music reading experiment. Hence, experienced readers had longer ETS when they approached the incongruent part of the notation and shorter ETS when they were in the process of struggling with the incongruent part. In addition to incongruent reading, the difference in the performance mode of the same music task associated with the translation of information and motor requirements was studied in the music reading experiment by comparing singing and playing from music scores. Despite the fact that the participants played incongruent melodies better than they sang them, the analysis of eye movement parameters allowed the researcher to discover that singing might be less cognitively demanding than playing. These findings are discussed within the proposed theoretical model of prediction and associated expertise theories.Odotusten vastaisten symbolien lukeminen: katseenseurantatutkimus nuotin- ja koodinluvusta Ihmiset luovat ja käyttävät erilaisia kieliä tallentaakseen, tarkastellakseen ja välittääkseen informaatiota. Luonnolliset kielet, kuten englanti, mahdollistavat ihmisten välisen kommunikaation arkisissa ja ammatillisissa tilanteissa. Sen sijaan symboliset kielet, kuten länsimainen nuottikirjoitus tai ohjelmointikielet, mahdollistavat erilaisten laitteiden, kuten soittimien tai tietokoneiden, operoinnin. Taitavien muusikkojen ja koodinlukijoiden silmänliikkeiden tutkimus on paljastanut joitakin samankaltaisuuksia siitä, miten näitä kahta symbolikieltä luetaan: toisin kuin tekstin lukemisessa, taitavat nuotin- ja koodinlukijat tekevät enemmän regressiivisiä, eli taaksepäin suuntautuvia silmänliikkeitä. Tämä väitöstutkimus on ensimmäinen tutkimushanke, jossa tarkastellaan nuotin- ja koodinlukua rinnakkain. Tutkimus keskittyy tiettyyn, molemmissa symbolikielissä tärkeään piirteeseen, eli taitoon selvitä lukemisen aikana notaatiossa havaittuun yllättävään informaatioon. Sekä musiikin että ohjelmoinnin aloilla tätä taitoa tarvitaan silloin, kun lukijan täytyy käsitellä yllättäviä melodisia kuvioita musiikkikappaletta lukiessaan tai etsiä virheitä koodista. Tällä väitöstutkimuksella oli kolme päätavoitetta: (1) teoreettinen pohdinta nuotin- ja koodinluvun yhtäläisyyksistä ja eroavaisuuksista ja erityisesti siitä, miten taitavat lukijat muodostavat ennakko-oletuksia lukemastaan symbolien tunnistamisen helpottamiseksi; (2) menetelmien kehittäminen (nuotinluvussa) ja luominen (koodinluvussa) inkongruenttien, eli epäyhdenmukaisten, kuvioiden lukemisen tutkimukseen; ja (3) taitavien nuotinlukijoiden ja yhden taitavan koodinlukijan kognitiivisen prosessoinnin tutkiminen silloin, kun lukijat käsittelevät inkongruenttia informaatiota. Yllättävät elementit tutussa visuaalisessa materiaalissa vaikeuttavat kyseessä olevan materiaalin prosessointia. Artikkelissa I pohditaan nuotin- ja koodinluvun teoreettisia eroja ja eroavaisuuksista ja sitä, miten taitavat lukijat muodostavat ennakko-oletuksia lukiessaan. Ehdotettu ennustusmalli, jonka avulla vastataan päätavoitteeseen 1, sisältää kolme molemmille symbolikielille yhteistä komponenttia: (1) tiedon kielijärjestelmästä, (2) tiedon merkityksestä ja (3) tiedon kontekstista. Tämän lisäksi malli sisältää kaksi komponenttia, joissa nuotin- ja koodinluku eroavat toisistaan: (4) informaation kääntäminen laitteelle ja (5) temporaaliset ja motoriset vaatimukset. Tässä väitöstutkimuksessa esiteltävät empiiriset osahankkeet olivat ensiaskelia ennustusmallin komponenttien tutkimuksessa. Kahdessa osahankkeessa tarkasteltiin yksityiskohtaisesti tilanteita, joissa ennakko-oletuksia voi hyödyntää tavalliseen tapaan (kongruentti notaatio) ja kun odotukset eivät toteudu (inkongruentti notaatio). Tässä väitöstutkimuksessa kehitettiin koeasetelmia nuotin- ja koodinluvun aikaisten, yllättävien ja inkongruenttien kuvioiden lukemisen tutkimusta varten (päätavoite 2). Nuotinlukuaiheinen koeasetelma pohjautui aikaisempaan tutkimukseen (Penttinen et al., 2015), jossa epäyhdenmukaisuuksia sijoitettiin tuttuun ”Maijall’ oli karitsa” –melodiaan ja soittosuoritusten ajoitusta kontrolloitiin metronomin avulla. Osatutkimus 1 kehitti tätä asetelmaa edelleen esittämällä tutun kappaleen osallistujille kahdessa eri sävellajissa ja pyytämällä osallistujia toteuttamaan melodia kahdella eri tavalla, soittaen ja laulaen. Osatutkimus 1 keskittyi siis ennustusmallin ensimmäiseen, toiseen, neljänteen ja viidenteen komponenttiin nuotinlukemisen näkökulmasta. Osatutkimuksessa 1 tutkittiin kuinka taitavat nuotinlukijat prosessoivat kongruenttien ja inkongruenttien nuottisymbolien merkityksiä. Tämän lisäksi osahanke selvitti sitä, miten nuottiinformaatio käännettiin kahdelle motorisilta vaatimuksiltaan erilaiselle ”soittimelle” (pianonsoitto ja laulaminen). Kognitiivisia prosesseja inkongruentin materiaalin lukemisen aikana kuvailtiin kolmen eri silmänliikemuuttujan turvin: lukuprosessin ajallista etenemistä selvitettiin eye-time span –mittarin (ETS) avulla, kognitiivista työmäärää mittaamalla pupillin koon vaihtelua, ja fiksaatioiden kestot kertoivat huomion kohdistumisesta notaation eri osiin ensilukemisen aikana. Koodinlukuun keskittyvä osatutkimus 2 suunniteltiin osatutkimuksen 1 koeasetelman pohjalta ja epäyhdenmukaisuudet sijoitettiin jälleen tuttuun notaatioon. Koodinlukukokeessa nuotinlukukokeen ”Maijall’ oli karitsa” –melodian tilalle valittiin hyvin tunnettu kuplalajittelualgoritmi, ja myös koodinlukutehtävässä kontrolloitiin ajankäyttöä. Tapaustutkimuksessa selvitettiin, miten kokenut ohjelmoija luki lajittelualgoritmia silloin kun siinä joko oli tai ei ollut inkongruentteja kohtia. Näin voitiin tarkastella ennustusmallin ensimmäistä ja toista komponenttia koodinlukemisen näkökulmasta. Tapaustutkimus keskittyi erityisesti tilanteeseen, jossa kokenut lukija ohitti yllättävät elementit notaatiossa ja tulkitsi inkongruentin algoritmin oikeaksi ja alkuperäiseksi (ns. proof-readers’ error). Tämän lisäksi osatutkimuksessa 2 testattiin koodinlukututkimuksiin sopivia analyysiyksiköitä vertaamalla kahta vaihtoehtoa, rivejä ja elementtejä, ja esiteltiin sakkadien nopeus kognitiivisen työmäärän tarkasteluun sopivana, koodinlukututkimuksille uutena mittarina. Osatutkimusten 1 ja 2 perusteella vastattiin päätavoitteeseen 3. Osatutkimuksissa selvisi, että sekä nuotin- että koodinlukutilanteissa inkongruentit kohdat notaatiossa johtivat muutoksiin fiksaatio- ja kognitiivisen työmäärän mittareissa (pupillin koko ja sakkadin nopeus). Toisin kuin koodinluvussa, tiukat temporaaliset rajoitteet säätelevät inkongruenssin prosessointia nuotinluvun aikana. Tästä syystä nuotinlukukokeessa hyödynnettiin ETS-mittaria, joka kertoo katseen kohdan ja musiikillisen ajan välisestä etäisyydestä. Taitavien nuotinlukijoiden ETS oli pidempi, kun he lukiessaan lähestyivät inkongruenttia kohtaa, ja lyhyempi, kun he soittivat tätä samaista kohtaa. Inkongruentin kohdan lukemisen lisäksi tutkittiin kahta erilaista esitystapaa (laulaminen ja soittaminen), sillä esitystapa liittyy nuotti-informaation kääntämiseen oikeiksi motoriksiksi liikkeiksi. Vaikka osallistujat soittivat inkongruentit melodiat paremmin kuin he lauloivat ne, silmänliikkeiden tarkastelu osoitti että laulaminen saattoi silti olla osallistujille kognitiivisesti vähemmän vaativaa kuin soittaminen. Näitä havaintoja pohditaan väitöskirjatutkimuksessa esitetyn ennustusmallin sekä asiantuntijuusteorioiden valossa

Rethinking Productivity in Software Engineering

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology. ; Collects the wisdom of software engineering thought leaders in a form digestible for any developer Shares hard-won best practices and pitfalls to avoid An up to date look at current practices in software engineering productivit