Towards the Automation of Migration and Safety of Third-Party Libraries

The process of migration from one library to a new, different library is very complex. Typically, the developer needs to find functions in the new library that are most adequate in replacing the functions of the retired library. This process is subjective and time-consuming as the developer needs to fully understand the documentation of both libraries to be able to migrate from an old library to a new one and find the right matching function(s) if exists. Our goal is helping the developer to have better experiences with library migration by identifying the key problems related to this process. Based on our critical literature review, we identified three main challenges related to the automation of library migration: (1) the mining of existing migrations, (2) learning from these migrations to recommend them in similar contexts, and (3) guaranteeing the safety of the recommended migrations

API Recommendation Using Domain And Source Code Knowledge

The process of migration the old retired API(Application Programming Interface) with new and most to up to date one, know as API migration. Developers need to fully understand the documentation for the retired (replaced) library and the new (replacing) library to do the appropriate migration. This manual process is complex, error-prone, and costly for companies. There have been many studies focused on the automation recommendation between different method mapping for different libraries. These studies focused on the recommendations between methods from different programming languages while non of them focused on the recommendations between methods of libraries that belong to the same programming language. At times, one of the studies indicates automatic recommendation when mapping two different methods libraries that belong to the same programming language by using domain knowledge(method description, method parameters|name). In this thesis, we investigated the mapping between two methods of library migrations by using the domain knowledge and source code documentation. In order to be able to obtain these scenarios, we propose the RAPIM++ machine learning approach which recommends a correct mapping between source and target methods of three-party libraries using domain knowledge and source code knowledge. Our main contribution in this studywas, build a model which depends on existing library changes done manually from previous developers in different open source projects in java programming language then use features related to source code implementation, the similarity between method signatures and methods documentation to predict correct method mapping between two methods level library migration. Our result was RAPIM++ was able to successfully mapping between two methods from different third-party libraries with a rate of accuracy score of 84.4%. Additionally, our approach could able to recommend the libraries that absent the documentations since it relies on the source code knowledge along with the main knowledge. We can conclude from these results that RAPIM++ able to recommend third-party libraries with or without documentation, so though libraries that are not well known and do not belong to popular frameworks, can find comprehensive recommendations when using our model. Furthermore, RAPIM++ provides the research and industry community with a lightweight web service that available publicly to make method mapping between third - part libraries an easy task for developers

Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.Comment: Accepted by FSE 202

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Rohelisema tarkvaratehnoloogia poole tarkvaraanalüüsi abil

Mobiilirakendused, mis ei tühjenda akut, saavad tavaliselt head kasutajahinnangud. Mobiilirakenduste energiatõhusaks muutmiseks on avaldatud mitmeid refaktoreerimis- suuniseid ja tööriistu, mis aitavad rakenduse koodi optimeerida. Neid suuniseid ei saa aga seoses energiatõhususega üldistada, sest kõigi kontekstide kohta ei ole piisavalt energiaga seotud andmeid. Olemasolevad energiatõhususe parandamise tööriistad/profiilid on enamasti prototüübid, mis kohalduvad ainult väikese alamhulga energiaga seotud probleemide suhtes. Lisaks käsitlevad olemasolevad suunised ja tööriistad energiaprobleeme peamiselt a posteriori ehk tagantjärele, kui need on juba lähtekoodi sees. Android rakenduse koodi saab põhijoontes jagada kaheks osaks: kohandatud kood ja korduvkasutatav kood. Kohandatud kood on igal rakendusel ainulaadne. Korduvkasutatav kood hõlmab kolmandate poolte teeke, mis on rakendustesse lisatud arendusprotessi kiirendamiseks. Alustuseks hindame mitmete lähtekoodi halbade lõhnade refaktoreerimiste energiatarbimist Androidi rakendustes. Seejärel teeme empiirilise uuringu Androidi rakendustes kasutatavate kolmandate osapoolte võrguteekide energiamõju kohta. Pakume üldisi kontekstilisi suuniseid, mida võiks rakenduste arendamisel kasutada. Lisaks teeme süstemaatilise kirjanduse ülevaate, et teha kindlaks ja uurida nüüdisaegseid tugitööriistu, mis on rohelise Androidi arendamiseks saadaval. Selle uuringu ja varem läbi viidud katsete põhjal toome esile riistvarapõhiste energiamõõtmiste jäädvustamise ja taasesitamise probleemid. Arendame tugitööriista ARENA, mis võib aidata koguda energiaandmeid ja analüüsida Androidi rakenduste energiatarbimist. Viimasena töötame välja tugitööriista REHAB, et soovitada arendajatele energiatõhusaid kolmanda osapoole võrguteekeMobile apps that do not drain the battery usually get good user ratings. To make mobile apps energy efficient many refactoring guidelines and tools are published that help optimize the app code. However, these guidelines cannot be generalized w.r.t energy efficiency, as there is not enough energy-related data for every context. Existing energy enhancement tools/profilers are mostly prototypes applicable to only a small subset of energy-related problems. In addition, the existing guidelines and tools mostly address the energy issues a posteriori, i.e., once they have already been introduced into the code. Android app code can be roughly divided into two parts: the custom code and the reusable code. Custom code is unique to each app. Reusable code includes third-party libraries that are included in apps to speed up the development process. We start by evaluating the energy consumption of various code smell refactorings in native Android apps. Then we conduct an empirical study on the energy impact of third-party network libraries used in Android apps. We provide generalized contextual guidelines that could be used during app development Further, we conduct a systematic literature review to identify and study the current state of the art support tools available to aid green Android development. Based on this study and the experiments we conducted before, we highlight the problems in capturing and reproducing hardware-based energy measurements. We develop the support tool ‘ARENA’ that could help gather energy data and analyze the energy consumption of Android apps. Last, we develop the support tool ‘REHAB’ to recommend energy efficient third-party network libraries to developers.https://www.ester.ee/record=b547174

Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices

Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.Comment: To appear at EuroSys 202

Building Responsive Library Collections with the Getting It System Toolkit

The Getting It System Toolkit (GIST), a suite of free and open source tools & software, leverages systems to optimize library acquisitions and deselection workflow, reducing the staff time necessary to make informed decisions and process materials. The Toolkit is divided into two functions:GIST for ILLiad consists of three components that enhance the ILLiad® interlibrary loan request management software: addons, webpage customizations, and the acquisitions manager. All three components may be selectively utilized in ILLiad, for instance, ILLiad web pages may be applied to enhance the end-user request interface to add full-text discovery, or an ILLiad Addon can help ILL’s purchase on demand program discover the best way to purchase items difficult to borrow. By combining all three and customizing these components for your library, you achieve significant benefits and optimize the combination of Acquisitions and ILL services.The GIST Gift & Deselection Manager (GDM) is designed to manage and streamline library workflow for processing gifts and evaluating materials for weeding. It is standalone open-source software that automates the gathering of data for evaluating donations; including holdings, edition comparisons, full-text, and other data. The GDM also enables collection managers to perform item-by-item deselection or use the batch analysis tool to create custom deselection reports for large weeding projects.Building Responsive Library Collections with the Getting It System Toolkit combines helpful how-tos from the developers themselves, and first-hand implementation accounts from users of these time-saving tools. The volume is split into the Toolkit’s use with ILLiad and GDM, providing easy reference for users. This manual is an invaluable resource to any library using, or considering using, the Getting It System Toolkit. With contributions by: Kerri Goergen-Doll, Oregon State University Eric Joslin, Washington University in St. Louis Ryan Litsey, Texas Tech University Micquel Little, Monroe Community College, formerly at St. John Fisher College Katherine Mason, Central Michigan University, formerly at Old Dominion University Kate Ross, St. John Fisher College Susanna Van Sant, Tompkins Cortland Community Collegehttps://knightscholar.geneseo.edu/idsproject-press/1001/thumbnail.jp

DepMiner: Automatic Recommendation of Transformation Rules for Method Deprecation

International audienceSoftware applications often depend on external libraries and must be updated when one of those libraries releases a new version. To make this process easier, library developers try to reduce the negative effect of breaking changes by deprecating the API elements before removing them and suggesting replacements to the clients. Modern programming languages and IDEs provide powerful tools for deprecations that can reference the replacement or incorporate the rules written by library developers and use them to automatically update the client code. However, in practice library developers often miss the deprecation opportunities and fail to document the deprecations. In this work, we propose to help library developers support their clients with better deprecations. We rely on the transforming deprecations offered by Pharo and use data mining to detect the missing deprecation opportunities and generate the transformation rules. We implemented our approach for Pharo in a prototype tool called DepMiner. We have applied our tool to five open-source projects and proposed the generated deprecations to core developers of those projects. 63 recommended deprecations were accepted as pull requests