Software developers and maintainers often need to locate code units responsible for a particular bug. A number of Information Retrieval (IR) techniques have been proposed to map natural language bug descriptions to the associated code units. The vector space model (VSM) with the standard tf-idf weighting scheme (VSM natural ), has been shown to outperform nine other state-of-the-art IR techniques. However, there are multiple VSM variants with different weighting schemes, and their relative performance differs for different software systems.Based on this observation, we propose to compose various VSM variants, modelling their composition as an optimization problem. We propose a genetic algorithm (GA) based approach to explore the space of possible compositions and output a heuristically near-optimal composite model. We have evaluated our approach against several baselines on thousands of bug reports from AspectJ, Eclipse, and SWT. On average, our approach (VSMcomposite ) improves hit at 5 (Hit@5), mean average precision (MAP), and mean reciprocal rank (MRR) scores of VSM natural by 18.4%, 20.6%, and 10.5% respectively. We also integrate our compositional model with AmaLgam, which is a stateof-art bug localization technique. The resultant model named AmaLgamcomposite on average can improve Hit@5, MAP, and MRR scores of AmaLgam by 8.0%, 14.4% and 6.5% respectively.

Low-power access points, such as pico base stations (BSs), femto BSs, and relays are introduced to the next generation cellular systems to enhance coverage and improve system capacity. Deploying low-power access points to offload the conventional macro BSs is deemed as a spectrum-and costefficient way to meet the sharp increase of traffic requirements of cellular networks. However, it also leads to heterogeneous network framework and raises new challenges for cell planning.In this paper, we study the minimum cost cell planning problem in such a heterogeneous network. Our optimization task is to select a subset of candidate sites to lay BSs, including macro BSs, pico BSs and relays, to minimize the total deployment cost while satisfying the rate requirements of the demand nodes (DNs) served by the cellular network. We prove that the general case of the formulated problem is APX-hard, where a DN is constrained to be associated with only one BS. However, if each DN can be served by multiple BSs, which is a reasonable case for practical cellular systems, we show it is not APX-hard and develop an approximation algorithm to work out promising solutions. Our proposed algorithm guarantees an approximation ratio of O(log R) to the global optimum, where R is the maximum achievable capacity of the BSs. Numerical results indicate that our proposal can significantly reduce the deployment cost of the cellular network with given rate requirements of DNs compared to other cell planning schemes.

Many spectrum-based fault localization measures have been proposed in the literature. However, no single fault localization measure completely outperforms others: a measure which is more accurate in localizing some bugs in some programs is less accurate in localizing other bugs in other programs. This paper proposes to compose existing spectrum-based fault localization measures into an improved measure. We model the composition of various measures as an optimization problem and present a search-based approach to explore the space of many possible compositions and output a heuristically near optimal composite measure. We employ two search-based strategies including genetic algorithm and simulated annealing to look for optimal solutions and compare the effectiveness of the resulting composite measures on benchmark software systems. Compared to individual spectrum-based fault localization techniques, our composite measures perform statistically significantly better.

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