Abstract [Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR’s first bug assignment without human intervention happened in April 2019. [Objective] Our study evaluates the adoption of TRR within its industrial context at Ericsson, i.e., we provide lessons learned related to the productization of a research prototype within a company. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. [Method] We conduct a preregistered industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug-tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. [Results] TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. Most importantly, some bug reports directly reach low-level modules without first having passed through fundamental root-cause analysis steps at higher levels. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. [Conclusions] TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.

To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems.In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model.Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model.This sampler utilizes all available sources of sparsity found in natural language-an important way to make computation efficient.We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel Pólya-urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that-contrary to popular belief in the topic modeling literature-partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.

We suggest a Bayesian approach to the problem of reducing bug turn-around time in large software development organizations. Our approach is to use classification to predict where bugs are located in components. This classification is a form of automatic fault localization (AFL) at the component level. The approach only relies on historical bug reports and does not require detailed analysis of source code or detailed test runs. Our approach addresses two problems identified in user studies of AFL tools. The first problem concerns the trust in which the user can put in the results of the tool. The second problem concerns understanding how the results were computed. The proposed model quantifies the uncertainty in its predictions and all estimated model parameters. Additionally, the output of the model explains why a result was suggested. We evaluate the approach on more than 50000 bugs.

Maintenance costs can be substantial for large organizations (several hundreds of programmers) with very large and complex software systems. By large we mean lines of code in the range of hundreds of thousands or millions. Our research objective is to improve the process of handling anomaly reports for large organizations. Specifically, we are addressing the problem of the manual, laborious and time consuming process of assigning anomaly reports to the correct design teams and the related issue of localizing faults in the system architecture. In large organizations, with complex systems, this is particularly problematic because the receiver of an anomaly report may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be assigned to the wrong team in the organization, causing delays and unnecessary work. We have so far developed two machine learning prototypes to validate our approach. The latest, a re-implementation and extension, of the first is being evaluated on four large systems at Ericsson AB. Our main goal is to investigate how large software development organizations can significantly improve development efficiency by replacing manual anomaly report assignment and fault localization with machine learning techniques. Our approach focuses on training machine learning systems on anomaly report databases; this is in contrast to many other approaches that are based on test case execution combined with program sampling and/or source code analysis.

Maintenance costs can be substantial for organizations with very large and complex software systems. This paper describes research for reducing anomaly report turnaround time which, if successful, would contribute to reducing maintenance costs and at the same time maintaining a good customer perception. Specifically, we are addressing the problem of the manual, laborious, and inaccurate process of assigning anomaly reports to the correct design teams. In large organizations with complex systems this is particularly problematic because the receiver of the anomaly report from customer may not have detailed knowledge of the whole system. As a consequence, anomaly reports may be wrongly routed around in the organization causing delays and unnecessary work. We have developed and validated machine learning approach, based on stacked generalization, to automatically route anomaly reports to the correct design teams in the organization. A research prototype has been implemented and evaluated on roughly one year of real anomaly reports on a large and complex system at Ericsson AB. The prediction accuracy of the automation is approaching that of humans, indicating that the anomaly report handling time could be significantly reduced by using our approach.