It is not uncommon to hear laments about how long it takes to build software systems and how often, once built, those systems fail to meet the needs and desires of users. Given that attention has been paid to how we build large software systems for over fifty years, you might wonder why we have not figured out how to build the systems people want in a reasonable amount of time. To put the problems into perspective, fifty years is half the life-span of a Galapagos turtle and many software systems may be amongst the most complex systems ever built by humans. In that light, perhaps it is not surprising that we have not figured it all out. In this talk, I will explore what productivity means to software developers, how we might track the value that is delivered in software produced by developers and how we might begin to think about measuring the productive delivery of effective software.

Continue reading

Inference is the production stage of machine learning workflow in which a trained model is used to infer or predict with real world data. A recommendation system improves customer experience by displaying most relevant items based on historical behavior of a customer. Machine learning models built for recommendation systems are deployed either on-premise or migrated to a cloud for inference in real time or batch. A recommendation system should be cost effective while honoring service level agreements (SLAs). In this work we discuss on-premise implementation of our recommendation system called iPrescribe. We show a methodology to migrate on-premise implementation of recommendation system to a cloud using ML workflow. We also present our study on performance of recommendation system model when deployed on different types of virtual instances.

Continue reading

Modern software systems are highly customizable through configuration options. The sheer size of the configuration space makes it challenging to understand the performance influence of individual configuration options and their interactions under a specific usage scenario. Software with poor performance may lead to low system throughput and long response time. This paper presents ConfProf, a white-box performance profiling technique with a focus on configuration options. ConfProf helps developers understand how configuration options and their interactions influence the performance of a software system. The approach combines dynamic program analysis, machine learning, and feedback-directed configuration sampling to profile the program execution and analyze the performance influence of configuration options. Compared to existing approaches, ConfProf uses a white-box approach combined with machine learning to rank performance-influencing configuration options from execution traces. We evaluate the approach with 13 scenarios of four real-world, highly-configurable software systems. The results show that ConfProf ranks performance-influencing configuration options with high accuracy and outperform a state of the art technique.

Continue reading

Real time stream processing demands processed outcomes in minimal latency. Massive streams are generated in real time where linear relationship is determined using correlation. Existing approaches are used for correlating static data sets such as, Kandell, Pearson, Spearman etc. These approaches are insufficient to solve noise free online correlation. In this paper, we propose an online ordinal correlation approach having functionalities such as single pass, avoiding recalculation from scratch, removing outliers, and low memory requirements. In this approach, Compare Reduce Aggregate (CRA) algorithm is used for determining association between two feature vectors in real time using single scanning technique. Time and space complexities in CRA algorithm are measured as O(n) and O(1), respectively. This algorithm is used for reducing noise or error in a stream and used as a replacement of rank based correlation. It is recommended to have distinct elements and less variability in the streams for gaining maximum performance of this algorithm.

Continue reading

Nowadays we experience a paradigm shift in our society, where every item around us is becoming a computer facilitating life-changing applications like self-driving cars, tele-medicine, precision agriculture or virtual reality. On one hand, for the execution of such resource demanding applications we need powerful IT facilities. On the other hand, the requirements often include latencies below 100 ms or even below 10 ms -- what is called ''tactile internet''. To facilitate low latency computation has to be placed in the vicinity of the end users by utilizing the concept of Edge Computing. In this talk we explain the challenges of Edge systems in combination with tactile internet. We discuss the recent problems of geographically distributed machine learning applications and novel approaches to balance competing priorities like the energy efficiency and the staleness of the machine learning models. Available failure resilience mechanisms designed for Cloud computing or generic distributed systems cannot be applied to Edge systems due to timeliness, hyper heterogeneity and resource scarcity. Therefore, we discuss a novel machine learning based mechanism that evaluates the failure resilience of a service deployed redundantly on the edge infrastructure. Our approach learns the spatiotemporal dependencies between edge server failures and combines them with the topological information to incorporate link failures by utilizing the concept of the Dynamic Bayesian Networks (DBNs). Eventually, we infer the probability that a certain set of servers fails or disconnects concurrently during service runtime.

Continue reading

The effectiveness of search-based automated program repair is limited in the number of correct patches that can be successfully generated. There are two causes of such limitation. First, the search space does not contain the correct patch. Second, the search space is huge and therefore the correct patch cannot be generated (i.e., correct patches are either generated after incorrect plausible ones or not generated within the time budget).

Continue reading

Software development projects, in particular open source ones, heavily rely on the use of tools to support, coordinate and promote development activities. Despite their paramount value, they contribute to fragment the project data, thus challenging practitioners and researchers willing to derive insightful analytics about software projects. In this demo we present Perceval, a loyal helper able to perform automatic and incremental data gathering from almost any tool related with contributing to open source development, among others, source code management, issue tracking systems, mailing lists, forums, and social media. Perceval is an industry strong free software tool that has been widely used in Bitergia, a company devoted to offer commercial software analytics of software projects. It hides the technical complexities related to data acquisition and eases the definition of analytics. A video showcasing the main features of Perceval can be found at https://youtu.be/eH1sYF0Hdc8.

Continue reading

In Continuous Integration (CI), regression testing is constrained by the time between commits. This demands for careful selection and/or prioritization of test cases within test suites too large to be run entirely. To this aim, some Machine Learning (ML) techniques have been proposed, as an alternative to deterministic approaches. Two broad strategies for ML-based prioritization are learning-to-rank and what we call ranking-to-learn (i.e., reinforcement learning). Various ML algorithms can be applied in each strategy. In this paper we introduce ten of such algorithms for adoption in CI practices, and perform a comprehensive study comparing them against each other using subjects from the Apache Commons project. We analyze the influence of several features of the code under test and of the test process. The results allow to draw criteria to support testers in selecting and tuning the technique that best fits their context.

Continue reading

This demo presents usage and implementation details of SLEMI. SLEMI is the first tool to automatically find compiler bugs in the widely used cyber-physical system development tool Simulink via Equivalence Modulo Input (EMI). EMI is a recent twist on differential testing that promises more efficiency. SLEMI implements several novel mutation techniques that deal with CPS language features that are not found in procedural languages. This demo also introduces a new EMI-based mutation strategy that has already found a new confirmed bug in Simulink version R2018a. To increase SLEMI's efficiency further, this paper presents parallel generation of random, valid Simulink models. A video demo of SLEMI is available at https://www.youtube.com/watch?v=oliPgOLT6eY.

Continue reading