Studying the Integration Practices and the Evolution of Ad Libraries in the G...Md Ahasanuzzaman
In-app advertisements have become a major revenue for app developers in the mobile app economy. Ad libraries play an integral part in this ecosystem as app
developers integrate these libraries into their apps to display ads. However, little is known about how app developers integrate these libraries with their apps and how these libraries have evolved over time.
In this thesis, we study the ad library integration practices and the evolution of such libraries. To understand the integration practices of ad libraries, we manually study apps and derive a set of rules to automatically identify four strategies for integrating
multiple ad libraries. We observe that integrating multiple ad libraries commonly occurs in apps with a large number of downloads and ones in categories with a high percentage of apps that display ads. We also observe that app developers prefer to manage their own integrations instead of using off the shelf features of ad libraries for integrating multiple ad libraries.
To study the evolution of ad libraries, we conduct a longitudinal study of the 8 most popular ad libraries. In particular, we look at their evolution in terms of size, the main drivers for releasing a new ad library version, and their architecture. We observe that ad libraries are continuously evolving with a median release interval of 34 days. Some ad libraries have grown exponentially in size (e.g., Facebook Audience Network ad library), while other libraries have worked to reduce their size. To study the main drivers for releasing an ad library version, we manually study the release notes of the eight studied ad libraries. We observe that ad library developers continuously update their ad libraries to support a wider range of Android versions (i.e., to ensure that more devices can use the libraries without errors). Finally, we derive a reference architecture for ad libraries and study how the studied ad libraries diverged from this architecture during our study period.
Our findings can assist ad library developers to understand the challenges for developing ad libraries and the desired features of these libraries.
Andrea Di Sorbo, Sebastiano Panichella, Carol Alexandru, Corrado A. Visaggio, Gerardo Canfora, Harald Gall: SURF: Summarizer of User Reviews Feedback. Proceedings of the 39th IEEE International Conference on Software Engineering (ICSE 2017). Buenos Aires, Argentina. RANK: A*
Studying the Integration Practices and the Evolution of Ad Libraries in the G...Md Ahasanuzzaman
In-app advertisements have become a major revenue for app developers in the mobile app economy. Ad libraries play an integral part in this ecosystem as app
developers integrate these libraries into their apps to display ads. However, little is known about how app developers integrate these libraries with their apps and how these libraries have evolved over time.
In this thesis, we study the ad library integration practices and the evolution of such libraries. To understand the integration practices of ad libraries, we manually study apps and derive a set of rules to automatically identify four strategies for integrating
multiple ad libraries. We observe that integrating multiple ad libraries commonly occurs in apps with a large number of downloads and ones in categories with a high percentage of apps that display ads. We also observe that app developers prefer to manage their own integrations instead of using off the shelf features of ad libraries for integrating multiple ad libraries.
To study the evolution of ad libraries, we conduct a longitudinal study of the 8 most popular ad libraries. In particular, we look at their evolution in terms of size, the main drivers for releasing a new ad library version, and their architecture. We observe that ad libraries are continuously evolving with a median release interval of 34 days. Some ad libraries have grown exponentially in size (e.g., Facebook Audience Network ad library), while other libraries have worked to reduce their size. To study the main drivers for releasing an ad library version, we manually study the release notes of the eight studied ad libraries. We observe that ad library developers continuously update their ad libraries to support a wider range of Android versions (i.e., to ensure that more devices can use the libraries without errors). Finally, we derive a reference architecture for ad libraries and study how the studied ad libraries diverged from this architecture during our study period.
Our findings can assist ad library developers to understand the challenges for developing ad libraries and the desired features of these libraries.
Andrea Di Sorbo, Sebastiano Panichella, Carol Alexandru, Corrado A. Visaggio, Gerardo Canfora, Harald Gall: SURF: Summarizer of User Reviews Feedback. Proceedings of the 39th IEEE International Conference on Software Engineering (ICSE 2017). Buenos Aires, Argentina. RANK: A*
Recommending and localizing change requests for mobile apps based on user rev...Sebastiano Panichella
Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44 683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).
Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present
ARdoc, a tool which combines three techniques: (1) Natural Language Parsing,(2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the use-fulness of ARdoc in extracting important maintenance tasks for their mobile applications.
Analyzing Reviews and Code of Mobile Apps for Better Release PlanningSebastiano Panichella
The mobile applications industry experiences an unprecedented high growth, developers working in this context face a fierce competition in acquiring and retaining users.
They have to quickly implement new features and fix bugs, or risks losing their users to the competition. To achieve this goal they must closely monitor and analyze the user feedback they receive in form of reviews. However, successful apps can receive up to several thousands of reviews per day, manually analysing each of them is a time consuming task. To help developers deal with the large amount of available data, we manually analyzed the text of 1566 user reviews and defined a high and low level taxonomy containing mobile specific categories (e.g. performance, resources, battery, memory, etc.) highly relevant for developers during the planning of maintenance and evolution activities. Then we built the User Request Referencer (URR) prototype, using Machine Learning and Information Retrieval techniques, to automatically classify reviews according to our taxonomy and recommend for a particular review what are the source code files that need to be modified to handle the issue described in the user review. We evaluated our approach through an empirical study involving the reviews and code of 39 mobile applications. Our results show a high precision and recall of URR in organising reviews according to the defined taxonomy
What Would Users Change in My App? Summarizing App Reviews for Recommending ...Sebastiano Panichella
Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better
meeting user expectations. Thus, automated approaches have
been proposed in literature with the aim of reducing the effort
required for analyzing feedback contained in user reviews via
automatic classication/prioritization according to specific
topics. In this paper, we introduce SURF (Summarizer of
User Reviews Feedback), a novel approach to condense the
enormous amount of information that developers of popular
apps have to manage due to user feedback received on a
daily basis. SURF relies on a conceptual model for capturing
user needs useful for developers performing maintenance and
evolution tasks. Then it uses sophisticated summarisation
techniques for summarizing thousands of reviews and generating
an interactive, structured and condensed agenda of
recommended software changes. We performed an end-to-end
evaluation of SURF on user reviews of 17 mobile apps (5 of
them developed by Sony Mobile), involving 23 developers
and researchers in total. Results demonstrate high accuracy
of SURF in summarizing reviews and the usefulness of the
recommended changes. In evaluating our approach we found
that SURF helps developers in better understanding user
needs, substantially reducing the time required by developers
compared to manually analyzing user (change) requests and
planning future software changes.
You’ve released your app and it doesn’t sell? You are just wondering in the dark, updating it and still no success? You are screwed if you do not analyze your data! Examining the reasons for crashes and why people think it sucks will alleviate your suffering.
ASO Tips & Strategies For Organic App Growth During The LockdownMoEngage Inc.
With global businesses across all verticals rethinking their marketing spends, organic app growth has emerged as one of the top-most priorities for brands. But how do you approach app store optimization and resulting organic growth? Find out about the nuances of ASO that can help rank your app for quick wins and long term benefits.
Check out apps from verticals like news, ride-sharing (taxi), and health & fitness, on how they have adapted their offerings to better suit the market (and users) now and are winning.
You've spent a lot of time in building an app that provides a better experience for users accessing your content from their android smartphones, but what if target users fail to discover your awesome app? This talk will discuss some of the latest trends and results of experiments that will help app developers to gain more visibility of their app.
InMobi inDecode - Gaining App Visibility That MattersInMobi
Prajyot Mainkar, Director - Androcid Media, talking about 'Gaining App Visibility That Matters' at InMobi's inDecode event.
Learn more at indecode.inmobi.com
How to Succeed as a PM by fmr Native Instrument Dir of ProductProduct School
Main takeaways:
- Continuous discovery is the only way to do product
- Recruiting users will be hard, build relationships, or contact strangers in context
- Product development can be a struggle, agree on a recipe as a team, stick to it, and iterate
How to Succeed as a PM by Native Instruments fmr Dir of ProductProduct School
Main takeaways:
- Continuous discovery is the only way to do product
- Recruiting users will be hard, build relationships, or contact strangers in context
- Product development can be a struggle, agree on a recipe as a team, stick to it, and iterate
Tug of Perspectives: Mobile App Users vs Developers (pp. 83-94)
Sandeep Kaur Kuttal (#1), Yiting Bai (#2), Ezequiel Scott (∗3), Rajesh Sharma (∗4),
(#) Tandy School of Computer Science, University of Tulsa, USA.
(∗) University of Tartu, Estonia.
Vol. 18 No. 6 JUNE 2020 International Journal of Computer Science and Information Security
https://sites.google.com/site/ijcsis/vol-18-no-6-jun-2020
A Preliminary Field Study of Game Programming on Mobile DevicesTao Xie
Eric Anderson, Sihan Li, and Tao Xie. A Preliminary Field Study of Game Programming on Mobile Devices. Presented in Workshop on Programming for Mobile and Touch (PROMOTO 2013), Indianapolis, IN, October 2013.
Keyword Research: Finding 100's of Keywords With The Click Of A Button
Analyzing Keyword
Generating App Title
Writing your way for Ranking #1
How to use main keyword and secondary keyword within description
First Impressions are Crucial
Split Testing your Icon
Bonus- To Increase Downloads
App Reviews Importance and Methods
Get Featured
Partnership, Influencers, Marketing and PR
Social Media Marketing
Viral Loops & Referrals
Paid App Installs
LTV
Re-Engagement
How to prep an effective kickoff workshop in 3 steps – UX Camp CPHMagdalena Zadara
How to get the most of the start of a project, get your client onboard with what you are doing and make them feel like they are part of the team. This presentation will be most valuable to UI/UX designers who work directly with clients and have some control over their process.
What Is Data-Driven Product Development by Aaptiv Senior PMProduct School
In this talk, we talked about how to implement a full cycle of collaborative, data-driven product development. Lisa explained how to use qualitative and quantitative research to make product decisions, and how to facilitate design and ideation workshops to encourage team problem solving. This talk went deep into real-world case studies from the digital fitness space.
Slides from "Taking an Holistic Approach to Product Quality"Peter Marshall
This is the base material used during a half day workshop at expoQA 17 June 2019. Peter Marshall runs over the necessary technical, organisational, and improvement practices required to deliver high quality software. Deep dives into Continuous delivery, devops, organisational structures, agile and digital transformation.
How Can I Improve My App? Classifying User Reviews for Software Maintenance a...Sebastiano Panichella
App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task.
In this paper we present a taxonomy to classify app reviews
into categories relevant to software maintenance and evolution,
as well as an approach that merges three techniques: (1)
Natural Language Processing, (2) Text Analysis and (3) Sentiment
Analysis to automatically classify app reviews into the proposed
categories. We show that the combined use of these techniques
allows to achieve better results (a precision of 75% and a recall
of 74%) than results obtained using each technique individually
(precision of 70% and a recall of 67%).
Recommending and localizing change requests for mobile apps based on user rev...Sebastiano Panichella
Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce ChangeAdvisor, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44 683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of ChangeAdvisor in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that ChangeAdvisor is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).
Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present
ARdoc, a tool which combines three techniques: (1) Natural Language Parsing,(2) Text Analysis and (3) Sentiment Analysis to automatically classify useful feedback contained in app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrates that ARdoc correctly classifies feedback useful for maintenance perspectives in user reviews with high precision (ranging between84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%). While evaluating our tool developers of our study confirmed the use-fulness of ARdoc in extracting important maintenance tasks for their mobile applications.
Analyzing Reviews and Code of Mobile Apps for Better Release PlanningSebastiano Panichella
The mobile applications industry experiences an unprecedented high growth, developers working in this context face a fierce competition in acquiring and retaining users.
They have to quickly implement new features and fix bugs, or risks losing their users to the competition. To achieve this goal they must closely monitor and analyze the user feedback they receive in form of reviews. However, successful apps can receive up to several thousands of reviews per day, manually analysing each of them is a time consuming task. To help developers deal with the large amount of available data, we manually analyzed the text of 1566 user reviews and defined a high and low level taxonomy containing mobile specific categories (e.g. performance, resources, battery, memory, etc.) highly relevant for developers during the planning of maintenance and evolution activities. Then we built the User Request Referencer (URR) prototype, using Machine Learning and Information Retrieval techniques, to automatically classify reviews according to our taxonomy and recommend for a particular review what are the source code files that need to be modified to handle the issue described in the user review. We evaluated our approach through an empirical study involving the reviews and code of 39 mobile applications. Our results show a high precision and recall of URR in organising reviews according to the defined taxonomy
What Would Users Change in My App? Summarizing App Reviews for Recommending ...Sebastiano Panichella
Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better
meeting user expectations. Thus, automated approaches have
been proposed in literature with the aim of reducing the effort
required for analyzing feedback contained in user reviews via
automatic classication/prioritization according to specific
topics. In this paper, we introduce SURF (Summarizer of
User Reviews Feedback), a novel approach to condense the
enormous amount of information that developers of popular
apps have to manage due to user feedback received on a
daily basis. SURF relies on a conceptual model for capturing
user needs useful for developers performing maintenance and
evolution tasks. Then it uses sophisticated summarisation
techniques for summarizing thousands of reviews and generating
an interactive, structured and condensed agenda of
recommended software changes. We performed an end-to-end
evaluation of SURF on user reviews of 17 mobile apps (5 of
them developed by Sony Mobile), involving 23 developers
and researchers in total. Results demonstrate high accuracy
of SURF in summarizing reviews and the usefulness of the
recommended changes. In evaluating our approach we found
that SURF helps developers in better understanding user
needs, substantially reducing the time required by developers
compared to manually analyzing user (change) requests and
planning future software changes.
You’ve released your app and it doesn’t sell? You are just wondering in the dark, updating it and still no success? You are screwed if you do not analyze your data! Examining the reasons for crashes and why people think it sucks will alleviate your suffering.
ASO Tips & Strategies For Organic App Growth During The LockdownMoEngage Inc.
With global businesses across all verticals rethinking their marketing spends, organic app growth has emerged as one of the top-most priorities for brands. But how do you approach app store optimization and resulting organic growth? Find out about the nuances of ASO that can help rank your app for quick wins and long term benefits.
Check out apps from verticals like news, ride-sharing (taxi), and health & fitness, on how they have adapted their offerings to better suit the market (and users) now and are winning.
You've spent a lot of time in building an app that provides a better experience for users accessing your content from their android smartphones, but what if target users fail to discover your awesome app? This talk will discuss some of the latest trends and results of experiments that will help app developers to gain more visibility of their app.
InMobi inDecode - Gaining App Visibility That MattersInMobi
Prajyot Mainkar, Director - Androcid Media, talking about 'Gaining App Visibility That Matters' at InMobi's inDecode event.
Learn more at indecode.inmobi.com
How to Succeed as a PM by fmr Native Instrument Dir of ProductProduct School
Main takeaways:
- Continuous discovery is the only way to do product
- Recruiting users will be hard, build relationships, or contact strangers in context
- Product development can be a struggle, agree on a recipe as a team, stick to it, and iterate
How to Succeed as a PM by Native Instruments fmr Dir of ProductProduct School
Main takeaways:
- Continuous discovery is the only way to do product
- Recruiting users will be hard, build relationships, or contact strangers in context
- Product development can be a struggle, agree on a recipe as a team, stick to it, and iterate
Tug of Perspectives: Mobile App Users vs Developers (pp. 83-94)
Sandeep Kaur Kuttal (#1), Yiting Bai (#2), Ezequiel Scott (∗3), Rajesh Sharma (∗4),
(#) Tandy School of Computer Science, University of Tulsa, USA.
(∗) University of Tartu, Estonia.
Vol. 18 No. 6 JUNE 2020 International Journal of Computer Science and Information Security
https://sites.google.com/site/ijcsis/vol-18-no-6-jun-2020
A Preliminary Field Study of Game Programming on Mobile DevicesTao Xie
Eric Anderson, Sihan Li, and Tao Xie. A Preliminary Field Study of Game Programming on Mobile Devices. Presented in Workshop on Programming for Mobile and Touch (PROMOTO 2013), Indianapolis, IN, October 2013.
Keyword Research: Finding 100's of Keywords With The Click Of A Button
Analyzing Keyword
Generating App Title
Writing your way for Ranking #1
How to use main keyword and secondary keyword within description
First Impressions are Crucial
Split Testing your Icon
Bonus- To Increase Downloads
App Reviews Importance and Methods
Get Featured
Partnership, Influencers, Marketing and PR
Social Media Marketing
Viral Loops & Referrals
Paid App Installs
LTV
Re-Engagement
How to prep an effective kickoff workshop in 3 steps – UX Camp CPHMagdalena Zadara
How to get the most of the start of a project, get your client onboard with what you are doing and make them feel like they are part of the team. This presentation will be most valuable to UI/UX designers who work directly with clients and have some control over their process.
What Is Data-Driven Product Development by Aaptiv Senior PMProduct School
In this talk, we talked about how to implement a full cycle of collaborative, data-driven product development. Lisa explained how to use qualitative and quantitative research to make product decisions, and how to facilitate design and ideation workshops to encourage team problem solving. This talk went deep into real-world case studies from the digital fitness space.
Slides from "Taking an Holistic Approach to Product Quality"Peter Marshall
This is the base material used during a half day workshop at expoQA 17 June 2019. Peter Marshall runs over the necessary technical, organisational, and improvement practices required to deliver high quality software. Deep dives into Continuous delivery, devops, organisational structures, agile and digital transformation.
How Can I Improve My App? Classifying User Reviews for Software Maintenance a...Sebastiano Panichella
App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task.
In this paper we present a taxonomy to classify app reviews
into categories relevant to software maintenance and evolution,
as well as an approach that merges three techniques: (1)
Natural Language Processing, (2) Text Analysis and (3) Sentiment
Analysis to automatically classify app reviews into the proposed
categories. We show that the combined use of these techniques
allows to achieve better results (a precision of 75% and a recall
of 74%) than results obtained using each technique individually
(precision of 70% and a recall of 67%).
Bringing User-CenteredDesign Practices intoAgile Development Projectsabcd82
Bringing User-CenteredDesign Practices intoAgile Development Projects -This full day tutorial seeks to explain Agile Development\'s incremental release and iterative development strategy from the perspective of a user centered design practitioner. Practical advice is given on making Agile development more user-centric.
Live 2014 Survey Results: Open Source Development and Application Security Su...Sonatype
Over 3,300 participated! The final results of our 4th Annual Open Source and Application Security Survey are in. Adrian Lane from Securosis and Brian Fox from Sonatype provide a detailed breakdown of the findings from a developer and an application security perspective. They discuss policies, practices, and breaches as well as how organizations can use these results to create constructive conversations to feed their open source security management practices. Get more details on the survey - http://www.sonatype.com/about/2014-open-source-software-development-survey
Studying the Integration Practices and the Evolution of Ad Libraries in the G...SAIL_QU
In-app advertisements have become a major revenue for app developers in the mobile app economy. Ad libraries play an integral part in this ecosystem as app
developers integrate these libraries into their apps to display ads. However, little is known about how app developers integrate these libraries with their apps and how these libraries have evolved over time.
In this thesis, we study the ad library integration practices and the evolution of such libraries. To understand the integration practices of ad libraries, we manually study apps and derive a set of rules to automatically identify four strategies for integrating
multiple ad libraries. We observe that integrating multiple ad libraries commonly occurs in apps with a large number of downloads and ones in categories with a high percentage of apps that display ads. We also observe that app developers prefer to manage their own integrations instead of using off the shelf features of ad libraries for integrating multiple ad libraries.
To study the evolution of ad libraries, we conduct a longitudinal study of the 8 most popular ad libraries. In particular, we look at their evolution in terms of size, the main drivers for releasing a new ad library version, and their architecture. We observe that ad libraries are continuously evolving with a median release interval of 34 days. Some ad libraries have grown exponentially in size (e.g., Facebook Audience Network ad library), while other libraries have worked to reduce their size. To study the main drivers for releasing an ad library version, we manually study the release notes of the eight studied ad libraries. We observe that ad library developers continuously update their ad libraries to support a wider range of Android versions (i.e., to ensure that more devices can use the libraries without errors). Finally, we derive a reference architecture for ad libraries and study how the studied ad libraries diverged from this architecture during our study period.
Our findings can assist ad library developers to understand the challenges for developing ad libraries and the desired features of these libraries.
Improving the testing efficiency of selenium-based load testsSAIL_QU
Slides for a paper published at AST 2019:
Shahnaz M. Shariff, Heng Li, Cor-Paul Bezemer, Ahmed E. Hassan, Thanh H. D. Nguyen, and Parminder Flora. 2019. Improving the testing efficiency of selenium-based load tests. In Proceedings of the 14th International Workshop on Automation of Software Test (AST '19). IEEE Press, Piscataway, NJ, USA, 14-20. DOI: https://doi.org/10.1109/AST.2019.00008
An Automated Approach for Recommending When to Stop Performance TestsSAIL_QU
—Performance issues are often the cause of failures in
today’s large-scale software systems. These issues make performance
testing essential during software maintenance. However,
performance testing is faced with many challenges. One challenge
is determining how long a performance test must run. Although
performance tests often run for hours or days to uncover
performance issues (e.g., memory leaks), much of the data that is
generated during a performance test is repetitive. Performance
analysts can stop their performance tests (to reduce the time
to market and the costs of performance testing) if they know
that continuing the test will not provide any new information
about the system’s performance. To assist performance analysts
in deciding when to stop a performance test, we propose an
automated approach that measures how much of the data that is
generated during a performance test is repetitive. Our approach
then provides a recommendation to stop the test when the data
becomes highly repetitive and the repetitiveness has stabilized
(i.e., little new information about the systems’ performance is
generated).
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Studying the Dialogue Between Users and Developers of Free Apps in the Google Play Store
1. Studying the Dialogue Between Users and
Developers of Free Apps in the Google Play Store
Journal First Presentation | Empirical Software Engineering
Ahmed E. HassanSafwat Hassan Cor-Paul BezemerChakkrit (Kla)
Tantithamthavorn
2. We study user-developer interactions through the
distribution and rating mechanisms of the Google Play Store
2
How developers leverage the distribution
mechanism to publish emergency updates
3. We study user-developer interactions through the
distribution and rating mechanisms of the Google Play Store
3
How developers leverage the distribution
mechanism to publish emergency updates
The dialogue between users and developers
4. Mobile app stores offer rich data
4
The Google Play Store has around
3.6 million apps
March 2, 2018
Need to fix this update, it keeps on freezing.
User
5. For many years, developers were not able to
respond to user reviews
5
October 14, 2015
After listening to one podcast, sometimes I want to go back and
listen to it again. Where can I find the old shows that I have
already listened to?
User
6. Since recently, developers can engage in a
dialogue with the reviewer
6
October 14, 2015
After listening to one podcast, sometimes I want to go back and
listen to it again. Where can I find the old shows that I have
already listened to?
User
The app doesn't delete episode articles. If you don't see them it
means that you pressed on the toolbar 'eye' button to hide
read content. Press once more to display them.
Dev
7. Since recently, developers can engage in a
dialogue with the reviewer
7
October 14, 2015
After listening to one podcast, sometimes I want to go back and
listen to it again. Where can I find the old shows that I have
already listened to?
User
October 16, 2015
After talking to support, my questions were answered. It's a good
app
User
The app doesn't delete episode articles. If you don't see them it
means that you pressed on the toolbar 'eye' button to hide
read content. Press once more to display them.
Dev
8. Analyzing the dialogue between users and
developers
8
2) What are the common patterns of developer
responses?
1) What is the impact of responding to user
reviews on the review rating?
3) What drives a developer to respond?
9. A summary of the studied dataset
9
> 2K
Apps
> 355K
Changes in
reviews
> 128K
Developer
responses
> 4M
Reviews
10. There is a great benefit in
responding to reviews
10
Six times more likely to lead to
a rating increase
11. There is a great benefit in
responding to reviews
11
In 84% of the cases of rating increase,
users increase their rating to four stars
or five stars
Six times more likely to lead to
a rating increase
12. We identified three main reasons for rating
increase
12
Developer guides the user to solve
the reported issue without having
to deploy an app update
34%
13. We identified three main reasons for rating
increase
13
Developer guides the user to solve
the reported issue without having
to deploy an app update
34%
24% Developer deploys an update to
address the reported issue
14. We identified three main reasons for rating
increase
14
13%
Developer guides the user to solve
the reported issue without having
to deploy an app update
34%
24% Developer deploys an update to
address the reported issue
Details of the solution are
communicated outside the store
15. Analyzing the dialogue between users and
developers
15
2) What are the common patterns of developer
responses?
1) What is the impact of responding to user reviews
on the review rating?
3) What drives a developer to respond?
16. Our approach for identifying the common
patterns of developer responses
16
Step 1:
Collect
metrics
We collected 6 metrics:
Review title length, Review text length, Days since last
release, Review rating, Positive sentiment, Negative
sentiment.
17. Our approach for identifying the common
patterns of developer responses
17
Step 2:
Build model
for every
app
Step 1:
Collect
metrics
We built 415 models.
18. Our approach for identifying the common
patterns of developer responses
18
Step 2:
Build model
for every
app
Step 3:
Extract the
key features
for each
model
Step 1:
Collect
metrics
We extracted 12 key features for each model.
19. Our approach for identifying the common
patterns of developer responses
19
Step 2:
Build model
for every
app
Step 3:
Extract the
key features
for each
model
Step 4:
Cluster the
models
Step 1:
Collect
metrics
20. We identified three patterns of developer
responses
20
Only negative reviews
21. We identified three patterns of developer
responses
21
Negative or longer reviews
Only negative reviews
22. We identified three patterns of developer
responses
22
Negative or longer reviews
Only negative reviews
Reviews which are posted shortly after the latest
release
23. Analyzing the dialogue between users and
developers
23
2) What are the common patterns of developer
responses?
1) What is the impact of responding to user reviews
on the review rating?
3) What drives a developer to respond?
24. Our approach for studying what drives a
developer to respond
24
• We manually read a statistically
representative random sample of user-
developer interaction episodes.
• We study the contents of responses to
understand better what drives developers to
respond to reviews
26. We identified four main drivers for responding
26
Thank the user63%
Ask for more details45%
27. We identified four main drivers for responding
27
Provide guidance25%
Thank the user63%
Ask for more details45%
28. We identified four main drivers for responding
28
Provide guidance25%
Thank the user63%
Ask for more details45%
Ask for endorsement24%
29. We identified four main drivers for responding
29
Provide guidance25%
Thank the user63%
Ask for more details45%
Ask for endorsement24%
30. For provide guidance, we observed that similar
responses can be used to provide FAQs
30
AppLock
The user asks how to use the “AppLock”
app to lock other apps
“Please open phone settings security apps
with usage access enable AppLock”
User
Dev
520
31. For provide guidance, we observed that similar
responses can be used to provide FAQs
31
PicsArt
The user complains that the app is very
slow
“HiVincent this issue can sometimes be
solved by clearing the cache.To do so go
to your device’s Settings - Apps - PicsArt
and tap ‘Clear data’ and ‘Clear cache’
User
Dev
271
AppLock
The user asks how to use the “AppLock”
app to lock other apps
“Please open phone settings security apps
with usage access enable AppLock”
User
Dev
520