Humberto Torres Marques-Neto

On the Benefits of Planning and Grouping Software Maintenance Requests

Despite its unquestionable importance, software maintenance usually has a negative image among software developers and even project managers. As a result, it is common to consider maintenance requests as short-term tasks that should be implemented as quick as possible to have a minimal impact for end-users. In order to promote software maintenance to a first-class software development activity, we first define in this paper a light weighted process -- called PASM (Process for Arranging Software Maintenance Requests) -- for handling maintenance as software projects. Next, we describe an in-depth evaluation of the benefits achieved by the PASM process at a real software development organization. For this purpose, we rely on a set of clustering analysis techniques in order to better understand and compare the requests handled before and after the adoption of the proposed process. Our results indicate that the number of projects created to handle maintenance requests has increased almost three times after this organization has adopted the PASM process. Furthermore, we also concluded that projects based on the PASM present a better balance between the various software engineering activities. For example, after adopting PASM the developers have dedicated more time to analysis and validation and less time to implementation and codification tasks.

SpaDeS: Detecting spammers at the source network

Despite the large variety and wide adoption of different techniques to detect and filter unsolicited messages (spams), the total amount of such messages over the Internet remains very large. Some reports point out that around 80% of all emails are spams. As a consequence, significant amounts of network resources are still wasted as filtering strategies are usually performed only at the email destination server. Moreover, a considerable part of these unsolicited messages is sent by users who are unaware of their spamming activity and may thus inadvertently be classified as spammers. In this case, these oblivious users act as spambots, i.e., members of a spamming botnet. This paper proposes a new method for detecting spammers at the source network, whether they are individual malicious users or oblivious members of a spamming botnet. Our method, called SpaDeS, is based on a supervised classification technique and relies only on network-level metrics, thus not requiring inspection of message content. We evaluate SpaDeS using real datasets collected from a Brazilian broadband ISP. Our results show that our method is quite effective, correctly classifying the vast majority (87%) of the spammers while misclassifying only around 2% of the legitimate users.

NextBug: a Bugzilla extension for recommending similar bugs

BackgroundDue to the characteristics of the maintenance process followed in open source systems, developers are usually overwhelmed with a great amount of bugs. For instance, in 2012, approximately 7,600 bugs/month were reported for Mozilla systems. Improving developers’ productivity in this context is a challenging task. In this paper, we describe and evaluate the new version of NextBug, a tool for recommending similar bugs in open source systems. NextBug is implemented as a Bugzilla plug-in and it was design to help maintainers to select the next bug he/she would fix.ResultsWe evaluated the new version of NextBug using a quantitative and a qualitative study. In the quantitative study, we applied our tool to 130,495 bugs reported for Mozilla products, and we consider as similar bugs that were handled by the same developer. The qualitative study reports the main results we received from a survey conducted with Mozilla developers and contributors. Most surveyed developers stated their interest in working with a tool like NextBug.ConclusionWe achieved the following results in our evaluation: (i) NextBug was able to provide at least one recommendation to 65% of the bugs in the quantitative study, (ii) in 54% of the cases there was at least one recommendation among the top-3 that was later handled by the same developer; (iii) 85% of Mozilla developers stated that NextBug would be useful to the Mozilla community.

Characterizing Dynamic Properties of the SopCast Overlay Network

Peer-to-Peer live video streaming systems are becoming increasingly popular. Nevertheless, in spite of various studies of client behavior aspects and system optimizations, the current knowledge about the dynamic properties of the system, particularly how the P2P overlay network changes over time during a live transmission, is still superficial. In this paper, we provide a characterization of the dynamic properties of a popular P2P live streaming media application, namely Sop Cast. We use complex network metrics to analyze how the structure of the network evolves over time from the perspective of individual nodes (local view) and of the whole network (global view). We find that Sop Cast peers may be clustered into three profiles based on their centrality properties in the network. Moreover, in spite of peers changing their partners over time, they tend to remain with the same centrality profile. Also, the global network structure tends to remain roughly stable over time, except for a decaying clustering coefficient. Our findings can be used to generate more realistic synthetic P2P workloads and to drive future system designs and simulations.

A quantitative approach for evaluating software maintenance services

The ever-increasing representativeness of software maintenance in the daily effort of software team requires initiatives for enhancing the activities accomplished to provide a good service for users who request a software improvement. This article presents a quantitative approach for evaluating software maintenance services based on cluster analysis techniques. The proposed approach provides a compact characterization of the services delivered by a maintenance organization, including characteristics such as service, waiting, and queue time. The ultimate goal is to help organizations to better understand, manage, and improve their current software maintenance process. We also report in this paper the usage of the proposed approach in a medium-sized organization throughout 2010. This case study shows that 72 software maintenance requests can be grouped in seven distinct clusters containing requests with similar characteristics. The in-depth analysis of the clusters found with our approach can foster the understanding of the nature of the requests and, consequently, it may improve the process followed by the software maintenance team.

An Empirical Study on Recommendations of Similar Bugs

The work to be performed on open source systems, whether feature developments or defects, is typically described as an issue (or bug). Developers self-select bugs from the many open bugs in a repository when they wish to perform work on the system. This paper evaluates a recommender, called NextBug, that considers the textual similarity of bug descriptions to predict bugs that require handling of similar code fragments. First, we evaluate this recommender using 69 projects in the Mozilla ecosystem. We show that for detecting similar bugs, a technique that considers just the bug components and short descriptions perform just as well as a more complex technique that considers other features. Second, we report a field study where we monitored the bugs fixed for Mozilla during a week. We sent mails to the developers who fixed these bugs, asking whether they would consider working on the recommendations provided by NextBug, 39 developers (59%) stated that they would consider working on these recommendations, 44 developers (67%) also expressed interest in seeing the recommendations in their bug tracking system.

On-the-fly extraction of hierarchical object graphs

Reverse engineering techniques are usually applied to extract concrete architecture models. However, these techniques usually extract models that just reveal static architectures, such as class diagrams. On the other hand, the extraction of dynamic architecture models is particularly useful for an initial understanding on how a system works or to evaluate the impact of possible maintenance tasks. This paper describes an approach to extract hierarchical object graphs (OGs) from running systems. The proposed graphs have the following distinguishing features: (a) they support the summarization of objects in domains, (b) they support the complete spectrum of relations and entities that are common in object-oriented systems, (c) they support multithreading systems, and (d) they include a language to alert about expected (or unexpected) relations between the extracted objects. We also describe the design and implementation of a tool for visualizing the proposed OGs. Finally, we provide two case studies. The first study shows how our approach can contribute to understand the running architecture of two systems (myAppointments and JHotDraw). The second study illustrates how OGs can help to locate defective software components in the JHotDraw system.

MobHet: Predicting human mobility using heterogeneous data sources

Abstract The literature is rich in mobility models that aim at predicting human mobility. Yet, these models typically consider only a single kind of data source, such as data from mobile calls or location data obtained from GPS and web applications. Thus, the robustness and effectiveness of such data-driven models from the literature remain unknown when using heterogeneous types of data. In contrast, this paper proposes a novel family of data-driven models, called MobHet, to predict human mobility using heterogeneous data sources. Our proposal is designed to use a combination of features capturing the popularity of a region, the frequency of transitions between regions, and the contacts of a user, which can be extracted from data obtained from various sources, both separately and conjointly. We evaluate the MobHet models, comparing them among themselves and with two single-source data-driven models, namely SMOOTH and Leap Graph, while considering different scenarios with single as well as multiple data sources. Our experimental results show that our best MobHet model produces results that are better than or at least comparable to the best baseline in all considered scenarios, unlike the previous models whose performance is very dependent on the particular type of data used. Our results thus attest the robustness of our proposed solution to the use of heterogeneous data sources in predicting human mobility.

Visualizing and analyzing georeferenced workloads of mobile networks

The comprehension of human mobility patterns can help governments and private sectors to improve the quality of services offered to population. Characterizing and understanding the different workloads generated on mobile networks during different kind of events are a challenge for carriers, who should keep connectivity services always on. The visualization and analysis of such workloads can drive a better planning of the network infrastructure for different kinds of events. This work presents an approach for visualizing and analyzing georeferenced data of mobile networks usage, specially on events which generate unplanned workloads. In order to support our approach we built a system named the Geo-Data Visualizer. We show the capability of our system to visualize the mobile network usage as well as the dynamics and movement of network users during a period of time using three real datasets.

A Base Station Workload-Aware Dynamic Pricing Scheme for Mobile Internet Access

The ever-increasing traffic created by mobile Internet users has challenged ISPs to implement fair pricing scheme. Time-based pricing schemes could be used to address this problem giving users the opportunity to choose when they desire to spend their own resources to access the Internet. However, distinct base stations that make up a cellular data network often do not experience the same workload over time. Thus, a time-based pricing approach could create an undesirable situation to users who happen to be in a region covered by a base station that is currently overloaded. This paper presents a workload-aware dynamic time-based pricing scheme which uses historical workload of each base station to differentiate prices in order to foster the geographically distribution of the Internet accesses over the ISPs network. We evaluate the proposed pricing scheme via simulation using some human mobility models, and compare it with two other state-of-the-art pricing schemes. Our experimental results show that the proposed approach is able to better redistribute the network workload among a larger number of base stations in different human mobility models, when compared to the alternatives. This allows ISPs to accommodate a larger number of users since our approach help ISP to avoid the overload of base station resources.