A Search Engine for Finding and Reusing Architecturally Significant Code

Architectural tactics are the building blocks of software architecture. They describe solutions for addressing specific quality concerns, and are prevalent across many software systems. Once a decision is made to utilize a tactic, the developer must generate a concrete plan for implementing the tactic in the code. Unfortunately, this is a non-trivial task even for experienced developers. Developers often resort to using search engines, crowd-sourcing websites, or discussion forums to find sample code snippets to implement a tactic. A fundamental problem of finding implementation for architectural patterns/tactics is the mismatch between the high-level intent reflected in the descriptions of these patterns ,and low-level implementation details of them. To reduce this mismatch, we created a novel Tactic Search Engine called ArchEngine (ARCHitecture search ENGINE). ArchEngine can replace this manual Internet-based search process and help developers to reuse proper architectural knowledge and accurately implement tactics and patterns from a wide range of open source systems. ArchEngine helps developers find implementation examples of tactic for a given technical context. It uses information retrieval and program analysis techniques to retrieve applications that implement these design concepts. Furthermore, the search engine lists the code snippets where the patterns/tactics are located. Our case study with 21 professional software developers shows that ArchEngine is more effective than other search engines (e.g. SourceForge and Koders) in helping programmers to quickly find implementations of architectural tactics/patterns

Mining Architectural Information: A Systematic Mapping Study

Context: Mining Software Repositories (MSR) has become an essential activity in software development. Mining architectural information to support architecting activities, such as architecture understanding and recovery, has received a significant attention in recent years. However, there is an absence of a comprehensive understanding of the state of research on mining architectural information. Objective: This work aims to identify, analyze, and synthesize the literature on mining architectural information in software repositories in terms of architectural information and sources mined, architecting activities supported, approaches and tools used, and challenges faced. Method: A Systematic Mapping Study (SMS) has been conducted on the literature published between January 2006 and November 2021. Results: Of the 79 primary studies finally selected, 8 categories of architectural information have been mined, among which architectural description is the most mined architectural information; 12 architecting activities can be supported by the mined architectural information, among which architecture understanding is the most supported activity; 81 approaches and 52 tools were proposed and employed in mining architectural information; and 4 types of challenges in mining architectural information were identified. Conclusions: This SMS provides researchers with promising future directions and help practitioners be aware of what approaches and tools can be used to mine what architectural information from what sources to support various architecting activities.Comment: 68 pages, 5 images, 15 tables, Manuscript submitted to a Journal (2022

On opportunistic software reuse

The availability of open source assets for almost all imaginable domains has led the software industry toopportunistic design-an approach in which people develop new software systems in an ad hoc fashion by reusing and combining components that were not designed to be used together. In this paper we investigate this emerging approach. We demonstrate the approach with an industrial example in whichNode.jsmodules and various subsystems are used in an opportunistic way. Furthermore, to study opportunistic reuse as a phenomenon, we present the results of three contextual interviews and a survey with reuse practitioners to understand to what extent opportunistic reuse offers improvements over traditional systematic reuse approaches.Peer reviewe

Where and What do Software Architects blog?:An Exploratory Study on Architectural Knowledge in Blogs, and their Relevance to Design Steps

Software engineers share their architectural knowledge (AK) in different places on the Web. Recent studies show that architectural blogs contain the most relevant AK, which can help software engineers to make design steps. Nevertheless, we know little about blogs, and specifically architectural blogs, where software engineers share their AK. In this paper, we conduct an exploratory study on architectural blogs to explore their types, topics, and their AK. Moreover, we determine the relevance of architectural blogs to make design steps. Our results support researchers and practitioners to find and re-use AK from blogs.</p

Preserving the Quality of Architectural Tactics in Source Code

In any complex software system, strong interdependencies exist between requirements and software architecture. Requirements drive architectural choices while also being constrained by the existing architecture and by what is economically feasible. This makes it advisable to concurrently specify the requirements, to devise and compare alternative architectural design solutions, and ultimately to make a series of design decisions in order to satisfy each of the quality concerns. Unfortunately, anecdotal evidence has shown that architectural knowledge tends to be tacit in nature, stored in the heads of people, and lost over time. Therefore, developers often lack comprehensive knowledge of underlying architectural design decisions and inadvertently degrade the quality of the architecture while performing maintenance activities. In practice, this problem can be addressed through preserving the relationships between the requirements, architectural design decisions and their implementations in the source code, and then using this information to keep developers aware of critical architectural aspects of the code. This dissertation presents a novel approach that utilizes machine learning techniques to recover and preserve the relationships between architecturally significant requirements, architectural decisions and their realizations in the implemented code. Our approach for recovering architectural decisions includes the two primary stages of training and classification. In the first stage, the classifier is trained using code snippets of different architectural decisions collected from various software systems. During this phase, the classifier learns the terms that developers typically use to implement each architectural decision. These ``indicator terms\u27\u27 represent method names, variable names, comments, or the development APIs that developers inevitably use to implement various architectural decisions. A probabilistic weight is then computed for each potential indicator term with respect to each type of architectural decision. The weight estimates how strongly an indicator term represents a specific architectural tactics/decisions. For example, a term such as \emph{pulse} is highly representative of the heartbeat tactic but occurs infrequently in the authentication. After learning the indicator terms, the classifier can compute the likelihood that any given source file implements a specific architectural decision. The classifier was evaluated through several different experiments including classical cross-validation over code snippets of 50 open source projects and on the entire source code of a large scale software system. Results showed that classifier can reliably recognize a wide range of architectural decisions. The technique introduced in this dissertation is used to develop the Archie tool suite. Archie is a plug-in for Eclipse and is designed to detect wide range of architectural design decisions in the code and to protect them from potential degradation during maintenance activities. It has several features for performing change impact analysis of architectural concerns at both the code and design level and proactively keep developers informed of underlying architectural decisions during maintenance activities. Archie is at the stage of technology transfer at the US Department of Homeland Security where it is purely used to detect and monitor security choices. Furthermore, this outcome is integrated into the Department of Homeland Security\u27s Software Assurance Market Place (SWAMP) to advance research and development of secure software systems

An ontology-based approach to security pattern selection

Usually, the security requirements are addressed by abstracting the security problems arising in a specific context and providing a well proven solution to them. Security patterns incorporating proven security expertise solution to the recurring security problems have been widely accepted by the community of security engineering. The fundamental challenge for using security patterns to satisfy security requirements is the lack of defined syntax, which makes it impossible to ask meaningful questions and get semantically meaningful answers. Therefore, this paper presents an ontological approach to facilitating security knowledge mapping from security requirements to their corresponding solutions-security patterns. Ontologies have been developed usingWeb Ontology Language (OWL) and then incorporated into a security pattern search engine which enables sophisticated search and retrieval of security patterns using the proposed algorithm. Applying the introduced approach allows security novices to reuse security expertise to develop secure software system

Addressing Memory Bottlenecks for Emerging Applications

There has been a recent emergence of applications from the domain of machine learning, data mining, numerical analysis and image processing. These applications are becoming the primary algorithms driving many important user-facing applications and becoming pervasive in our daily lives. Due to their increasing usage in both mobile and datacenter workloads, it is necessary to understand the software and hardware demands of these applications, and design techniques to match their growing needs. This dissertation studies the performance bottlenecks that arise when we try to improve the performance of these applications on current hardware systems. We observe that most of these applications are data-intensive, i.e., they operate on a large amount of data. Consequently, these applications put significant pressure on the memory. Interestingly, we notice that this pressure is not just limited to one memory structure. Instead, different applications stress different levels of the memory hierarchy. For example, training Deep Neural Networks (DNN), an emerging machine learning approach, is currently limited by the size of the GPU main memory. On the other spectrum, improving DNN inference on CPUs is bottlenecked by Physical Register File (PRF) bandwidth. Concretely, this dissertation tackles four such memory bottlenecks for these emerging applications across the memory hierarchy (off-chip memory, on-chip memory and physical register file), presenting hardware and software techniques to address these bottlenecks and improve the performance of the emerging applications. For on-chip memory, we present two scenarios where emerging applications perform at a sub-optimal performance. First, many applications have a large number of marginal bits that do not contribute to the application accuracy, wasting unnecessary space and transfer costs. We present ACME, an asymmetric compute-memory paradigm, that removes marginal bits from the memory hierarchy while performing the computation in full precision. Second, we tackle the contention in shared caches for these emerging applications that arise in datacenters where multiple applications can share the same cache capacity. We present ShapeShifter, a runtime system that continuously monitors the runtime environment, detects changes in the cache availability and dynamically recompiles the application on the fly to efficiently utilize the cache capacity. For physical register file, we observe that DNN inference on CPUs is primarily limited by the PRF bandwidth. Increasing the number of compute units in CPU requires increasing the read ports in the PRF. In this case, PRF quickly reaches a point where latency could no longer be met. To solve this problem, we present LEDL, locality extensions for deep learning on CPUs, that entails a rearchitected FMA and PRF design tailored for the heavy data reuse inherent in DNN inference. Finally, a significant challenge facing both the researchers and industry practitioners is that as the DNNs grow deeper and larger, the DNN training is limited by the size of the GPU main memory, restricting the size of the networks which GPUs can train. To tackle this challenge, we first identify the primary contributors to this heavy memory footprint, finding that the feature maps (intermediate layer outputs) are the heaviest contributors in training as opposed to the weights in inference. Then, we present Gist, a runtime system, that uses three efficient data encoding techniques to reduce the footprint of DNN training.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146016/1/anijain_1.pd

Viitearkkitehtuuri tapahtumapohjaiselle mikropalveluarkkitehtuurille pilvipalveluissa

The emergence of public cloud computing platforms has had a profound effect on how software is being developed. To take advantage of many of the features of cloud platforms, software architecture of applications must aligned with the characteristics of cloud services. Where systems designed for traditional data center deployments have typically consisted of a single large application and a centralized data store, systems targeting cloud platform have become distributed applications. The microservice architecture is a software architecture style for building distributed systems that consist of autonomous services, each responsible for a single problem domain. Decomposing an application to individual components makes is possible to utilize cloud platform features such as scaling each part of the system according to load and performance. Enterprise applications are the context where the microservice architecture pattern is typically applied. These applications are large, long-lived, in state of constant change and highly integrated to other systems. But building complex enterprise applications as distributed systems poses architectural challenges on how to build a system that is evolvable, maintainable and understandable. This thesis describes patterns for building microservice systems that can scale to a large amount of services while retaining the autonomy the services and the maintainability of the system as a whole. A key factor in these patterns is the use of events for communication between the different components of the system. The thesis then presents a reference architecture on how such a system can be developed by utilizing managed services of a public cloud platform.Lisääntyvä pilvipalveluiden käyttö on vaikuttanut merkittävästi siihen, millaisia sovelluksia kehitetään. Sovelluksen arkkitehtuurin täytyy olla suunniteltu siten, että pilvipalveluiden ominaisuuksia voidaan hyödyntää. Sovellukset, jotka ovat suunniteltu ennen pilvipohjaisia arkkitehtuureja koostuvat tyypillisesti yhdestä suuresta asennettavasta komponentista ja keskitetystä tietovarastosta. Pilvipalveluiden myötä tämän mallin sijaan on ruvettu rakentamaan hajautettuja järjestelmiä. Mikropalveluarkkitehtuuri on ohjelmistoarkkitehtuuri, jossa hajautettu järjestelmä koostetaan yksittäisistä erillisistä palveluista. Jokainen palvelu vastaa järjestelmän tietystä toiminnosta tai osa-alueesta. Arkkitehtuuri, jossa sovellus on pilkottu pieniin autonomisiin komponentteihin mahdollistaa monien pilvipalveluiden ominaisuuksien (kuten kuorman mukaisen skaalauksen) käytn. Monimutkaiset yritysjärjestelmät ovat kenttä, jossa mikropalveluarkkitehtuuria tyypillisesti käytetään. Nämä järjestelmät ovat suuria, jatkuvan muutoksen alaisia ja moninaisin tavoin integroituneita useisiin muihin järjestelmiin. Monimutkaisten yritysjärjestelmien rakentaminen mikropalveluarkkitehtuurilla luo omat haasteensa siinä, miten järjestelmästä saadaan ylläpidettävä, jatkokehityskelpoinen ja ymmärrettävä. Tämä diplomityö kuvaa malleja mikropalvelujärjestelmien rakentamiseen siten, että järjestelmän kasvaessa yksittäiset mikropalvelut pysyvät erillisinä ja autonomisina sekä järjestelmä kokonaisuutena pystyy ylläpidettävänä. Avainrakenne näiden tavoitteiden saavuttamisessa on tapahtumien käyttö tiedon välittämisessä palveluiden välillä. Diplomityössä esitetään sitten viitearkkitehtuuri miten nämä mallit voidaan ottaa käyttöön julkisessa pilvipalvelussa

Exploiting the architectural characteristics of software components to improve software reuse

PhD ThesisSoftware development is a costly process for all but the most trivial systems. One of the commonly known ways of minimizing development costs is to re-use previously built software components. However, a significant problem that source-code re-users encounter is the difficulty of finding components that not only provide the functionality they need but also conform to the architecture of the system they are building. To facilitate finding reusable components there is a need to establish an appropriate mechanism for matching the key architectural characteristics of the available source-code components against the characteristics of the system being built. This research develops a precise characterization of the architectural characteristics of source-code components, and investigates a new way to describe how appropriate components for re-use can be identified and categorized.Umm Al- Qura University