“Many eyeballs make all bugs shallow” - referred to as Linus’ Law was framed by Raymond. The more is the number of developers working together on similar bugs for their resolution, the more easily and quickly will they get resolved. In this paper, we will be analyzing an open source dataset of 1000+ Android bugs, owned by 70 developers. Our results indicate that, for an arbitrary developer, the stronger is the developer network with other developers working in a similar environment, the more likely it is that they come across the same bugs during the development time. If they get access to the solution of the known bugs in the early phase of the development, then less time will be required for bug resolution and hence, in turn the quality of the software can be enhanced at a faster pace. We have done SEM analysis using Lavaan and have provided significant statistical evidence in support of our results.

Large scale software development ecosystems represent one of the most complex human enterprises. In such settings, developers are embedded in a web of shared concerns, responsibilities, and objectives at individual and collective levels. A deep understanding of the factors that influence developers to connect with one another is crucial in appreciating the challenges of such ecosystems as well as formulating strategies to overcome those challenges. We use real world data from multiple software development ecosystems to construct developer interaction networks and examine the mechanisms of such network formation using statistical models to identify developer attributes that have maximal influence on whether and how developers connect with one another. Our results challenge the conventional wisdom on the importance of particular developer attributes in their interaction practices, and offer useful insights for individual developers, project managers, and organizational decision-makers.

Studies of collaborating individuals engaged in collective enterprises usually focus on the individuals, rather than the links supporting their interaction. Accordingly, large scale software development ecosystems have also been examined primarily in terms of developer engagement. We posit that communication links between developers play a central role in the sustenance and effectiveness of such ecosystems. In this paper, we investigate whether and how developer attributes relate to the importance of the communication channels between them. We present a technique using 2nd order Markov models to extract features of interest of the links and apply the technique on data from a real-world project. Our statistical models - developed on records involving 900+ software developers, exchanging 20,000+ comments, across 500 units of work - offer surprising insights on factors associated with link importance, even after controlling for known effects. These results inform a deeper appreciation of the importance of links in large scale software development along with a number of practical implications.

Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system. Each user or item generally belong to at most one cluster. However, it may so happen that some users (boundary users) present in a particular cluster exhibit higher similarity with the preferences of the users residing in the nearby clusters than the ones present in their own cluster. Therefore, we propose a clustering based scalable recommendation algorithm that has a provision for switching a user from its original cluster to another cluster in order to provide more accurate recommendations. For a user belonging to multiple clusters, we aggregate recommendations from those clusters to which the user belongs in order to produce the final set of recommendations to that user. In this work, we propose two types of clustering, one on the basis of rating and the other on the basis of frequency and then compare their performances. Finally, we explore the applicability of cluster ensembles techniques in the proposed method. Our aim is to develop a recommendation framework that can scale well to handle large datasets without much affecting the recommendation quality. The outcomes of our experiments clearly demonstrate the scalability as well as efficacy of our method. It reduces the runtime of the baseline CF algorithm by a minimum of 58% and a maximum of 90% for MovieLens-10M dataset, and a minimum of 42% and a maximum of 84% for MovieLens-20M dataset. The accuracies of recommendations in terms of F1, MAP and NDCG metrics are also better than the existing clustering based recommender systems.

Abstract Large‐scale software systems are being increasingly built by distributed teams of developers who interact across geographies and time zones. Ensuring smooth knowledge transfer and the percolation of skills within and across such teams remain key challenges for organizations. Towards addressing this challenge, organizations often grapple with questions around whether and how repeat collaborations between members of a team relate to outcomes of important activities. In the context of this paper, the word ‘repeat interaction’ does not imply a greater number of interactions; it refers to repeat interaction between a pair of developers who have collaborated before. In this paper, we empirically examine such a question using real‐world data from three diverse development ecosystems, collectively involving 400,000+ units of work and 600,000+ comments exchanged between numerous developers. Our statistical models consistently establish a counter‐intuitive relation between repeat developer interaction and bug resolution times. Our experimental results show that more instances of repeat developer interactions over bug fixing are associated with more time taken for the bugs to be fixed. Given the expanse and variety of the underlying data, our results offer an unexpected set of insights on a key dynamic of collaboration in software development ecosystems. We discuss how these insights can influence the practice of large‐scale software development at individual, team and organizational levels.

As and when Moore's law started to falter, threedimensional integrated circuits (3D-ICs) emerged as a natural alternative to tackle the problem. Monolithic 3D-IC is one of the recent technology that was introduced in the 3D-IC technology, where MIVs (Monolithic Inter-tier Vias) are used for connecting modules that spread over multiple layers. In this paper, we focus on designing the multi layer partitioning tool before placement of cells in such a way that cost of wires is minimized along with balanced area of tiers. We have not put any constraint on the number of MIVs as it does not suffer from any disadvantages like TSV (Through Silicon Via). We implement our algorithm on a set of 3D-GSRC benchmark circuits present in the URL "3D GSRC benchmark http://cadlab.cs.ucla.edu/three d/3dic.html".

Majority of the e-commerce sites implement Recommender Systems (RS) to help users navigate through the large search space and assist their decision making process by suggesting products that the user may like. Collaborative Filtering (CF) is the most successful and widely used algorithm in the domain of RS. However, due to the exponential growth of the web in terms of both content and number of users, CF based RS face serious scalability issues. To alleviate this problem, we propose a clustering based CF approach using two hierarchical space partitioning data structures — K-d tree and Quadtree. We cluster or partition the users’ space of the system on the basis of user location and then use the resultant clusters for predicting ratings of a target user. Since the CF based recommendation algorithm is applied separately to the clusters and not on the entire rating data, it helps in bringing down the runtime of the algorithm substantially. We further measure spatial autocorrelation indices in the clusters to justify our clustering method. However, our objective is not only to reduce the runtime but also to maintain an acceptable recommendation quality. This requirement is rightly addressed by the proposed method which assures scalability, by processing very large datasets using the same computing resource. Moreover our proposed clustering scheme is oblivious of the underlying CF algorithm. Results from the extensive experiments conducted, show that our hierarchical clustering based recommendation approach reduces runtime of the standard CF algorithms by about 88%, 82%, 79% and 85% for MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor data respectively, while maintaining good recommendation quality.

Finding the density of a set of n points, especially where points are in IR 2 or IR 3 , has direct applications in thermal analysis of VLSI chips. In this paper, we consider identifying the maximum-density axes-parallel region for a set of weighted points in IR d for d ≥ 2, and show that it can be done in O( dn 2 ) time. We also consider finding the minimum-density axes-parallel region, and show that for IR 2 the problem can be solved in O( n 2 ) time.

Interest in the habits of influential individuals cuts across domains. As researchers, we are intrigued why few attain significant eminence in their fields, whereas many operate in obscurity. An empirical examination of this question has been made possible by the recent availability of large scale publication data. In this paper, we use information from the AMiner Paper Citation and Author Collaboration Networks to discern factors that relate to the impact of influential researchers across five domains in the computing discipline. We propose and apply a novel algorithm to identify influential vertices in co-authorship networks built from total corpora of 1,00,000+ papers and 72,000+ authors over a span of more than 50 years. The results from our study indicate that the impact of these influential researchers relate to a variety of factors. Surprisingly, we find evidence across the domains that higher impact is associated with lower levels of collaboration, and authority.

While 3D chips open up versatile potentialities in compact system design, they pose the challenge of testing the composite system, which consists of multiple cores, logic, and memory, interconnected across different layers of the chip. The test strategy for such chips must also take into account the issues of inherent power and thermal constraints, design of test-access mechanism (TAM), and the decision concerning pre-bond and post-bond test choices. Additionally, for post-bond testing, the constraints imposed by the limited use of TSVs, worsen the controllability and observability of the cores that are accessed through the inter-layer scan-paths. Thus, while designing the TAM architecture, the optimization of overall test time under the constraints of power and TSV-count, is needed. This paper presents a new technique for test-time reduction in post-bond core-based 3D-SOCs, considering certain constraints on test power and TAM width (i.e., bounds on TSVs). The proposed algorithm runs much faster compared to prior art, and our results on several ITC02 benchmarks reveal significant reduction in test-time for most of the cases.

In this paper, we deal with the problem of partitioning a set of coplanar points of more than one colors into monochromatic cells using minimum number of axis-parallel straight lines. It is first shown that the problem is NP-hard. A fast heuristic is then presented to solve this problem. Experimental results on randomly generated instances indicate that the proposed method is much faster than the existing techniques, with minor degradation in the cost of the partition.

This article addresses the problem of recursively bipartitioning a given floorplan F using monotone staircases. At each level of the hierarchy, a monotone staircase from one corner of F to its opposite corner is identified, such that (i) the two parts of the bipartition are nearly equal in area (or in the number of blocks), and (ii) the number of nets crossing the staircase is minimal. The problem of area-balanced bipartitioning is shown to be NP-hard, and a maxflow-based heuristic is proposed. Such a hierarchy may be useful to repeater placement in deep-submicron physical design, and also to global routing.

We explore non-robust untestability of paths based on redundant stuck-at faults. Such untestability classification is necessary for a path to be ignored in timing verification and delay testing. A recent result states that redundant stuck-at-0 (s-a-0) and stuck-at-1 (s-a-1) faults of a line imply untestability of rising and falling delay faults, respectively, for all paths through that line. We find that this result only establishes robust untestability of paths. Starting with known examples, where a non-robust test can exist for some paths that pass through the site of a redundant stuck-at fault, we examine various classes of stuck-at fault redundancies. We prove that: (1) an unexcitable or undrivable redundant s-a-0 (s-a-1) fault will make all paths through the fault site non-robustly delay-untestable for rising (falling) transition, and (2) an unobservable fault site (causing both s-a-0 and s-a-1 faults to be redundant) can only classify the passing paths as robustly delay-untestable, Finally, we show that two singly-untestable paths, passing through the sites of separate redundant single stuck-at faults, may form a multiply-testable pair of paths provided the two redundant single stuck-at faults have a multi-fault test.